Methods of Diagnosing Infectious Disease Pathogens and Their Drug Sensitivity

ABSTRACT

The specification relates generally to methods of detecting, diagnosing, and/or identifying pathogens, e.g., infectious disease pathogens and determining their drug sensitivity and appropriate methods of treatment. This invention also relates generally to methods of monitoring pathogen infection in individual subjects as well as larger populations of subjects.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Nos. 61/307,669, filed on Feb. 24, 2010, and 61/323,252,filed on Apr. 12, 2010, the entire contents of which are herebyincorporated by reference in their entireties.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Grant Number3U54-A1057159-0651 awarded by the National Institutes of Health. TheGovernment has certain rights in the invention.

TECHNICAL FIELD

The invention relates, inter alia, to methods of detecting, diagnosing,and/or identifying pathogens, e.g., infectious disease pathogens, anddetermining their sensitivity to known or potential treatments.

BACKGROUND

The development of molecular diagnostics has revolutionized care in mostmedical disciplines except infectious disease, where they have failed toplay a widespread, transforming role. The reliance on slow culturemethods is particularly frustrating in the current crisis of antibioticresistance as the development of molecular tools to rapidly diagnose theinciting pathogen and its drug resistance profile would transform themanagement of bacterial, fungal, viral, and parasitic infections,guiding rapid, informed drug treatment in an effort to decreasemortality, control health care costs, and improve public health controlof escalating resistance among pathogens. In U.S. hospitals alone, 1.7million people acquire nosocomial bacterial infection and 99,000 dieevery year, with 70% of these infections due to bacteria resistant to atleast one drug and an estimated annual cost of $45 billion (Klevens etal., 2002. Public Health Rep. 2007;122(2):160-6; Klevens et al., ClinInfect Dis. 2008;47(7):927-30; Scott, The Direct Medical Costs ofHealthcare-Associated Infection in U.S. Hospitals and the Benefits ofPrevention. In: Division of Healthcare Quality Promotion NCfP, Detectionand Control of Infectious Diseases, editor. Atlanta: CDC, 2009).However, the problem is not limited to the U.S. and microbial resistancenow impacts the majority of common bacterial infections globally. Globalspread of methicillin-resistant S. aureus (MRSA), multi-drug resistanttuberculosis (MDR-TB), and increasingly drug resistant Gram-negativeorganisms prompted the formulation of an action plan focusing onsurveillance, prevention and control, research and product development(US action plan to combat antimicrobial resistance. Infect Control HospEpidemiol. 2001;22(3):183-4). However, minimal progress has been made onany of these fronts.

Prompt administration of the appropriate antibiotic has repeatedly beenshown to minimize mortality in patients with severe bacterialinfections, whether within the hospital setting with nosocomialpathogens such as E. faecium, S. aureus, K. pneumoniae, A. baumanii, P.aeruginosa, and Enterobacter species, or in resource-poor settings withpathogens such as tuberculosis (TB) (Harbarth et al., Am J Med.2003;115(7):529-35; Harries et al., Lancet. 2001;357(9267):1519-23; Lawnet al., Int J Tuberc Lung Dis. 1997;1(5):485-6). However, becausecurrent diagnostic methods involving culture and sub-culture oforganisms can take several days or more to correctly identify both theorganism and its drug susceptibility pattern, physicians have resortedto increasing use of empiric broad-spectrum antibiotics, adding to theselective pressure for resistance and increasing the associatedhealth-care costs. A point of care diagnostic to rapidly (e.g., lessthan 1 hour) detect pathogens and their resistance profiles is urgentlyneeded and could dramatically change the practice of medicine. Someeffort into designing DNA- or PCR-based tests has resulted in tools thatare able to identify pathogens rapidly with low detection limits.However, global use of these tools is currently limited due to cost anddemand for laboratory infrastructure and to the inherent insensitivityof PCR-based methods in the setting of crude samples that are not easilyamenable to the required enzymology. Molecular approaches to determiningdrug resistance have been even more limited, available for someorganisms (e.g., MRSA, TB) in very limited ways, based on defining thegenotype of the infecting bacteria relative to known resistanceconferring mutations. This method however, requires fairly comprehensiveidentification of all resistance conferring single nucleotidepolymorphisms (SNPs) for the test to have high sensitivity (Carroll etal., Mol Diagn Ther. 2008;12(1):15-24).

SUMMARY

The present invention is based, at least in part, on the discovery ofnew methods of diagnosing disease, identifying pathogens, and optimizingtreatment based on detection of mRNA, e.g., in crude, non-purifiedsamples. The methods described herein provide rapid and accurateidentification of pathogens in samples, e.g., clinical samples, andallow for the selection of optimal treatments based on drug sensitivitydeterminations.

In one aspect, the invention features methods of determining the drugsensitivity of a pathogen, e.g., a disease-causing organism such as abacterium, fungus, virus, or parasite. The methods include providing asample comprising a pathogen and contacting the sample with one or moretest compounds, e.g., for less than four hours, to provide a testsample. The test sample can be treated under conditions that releasemRNA from the pathogen into the test sample and the test sample isexposed to a plurality of nucleic acid probes, comprising a plurality ofsubsets of probes, wherein each subset comprises one or more probes thatbind specifically to a target mRNA that is differentially expressed inorganisms that are sensitive to a test compound as compared to organismsthat are resistant, wherein the exposure occurs for a time and underconditions in which binding between the probe and target mRNA can occur.The method comprises determining a level of binding between the probeand target mRNA, thereby determining a level of the target mRNA; andcomparing the level of the target mRNA in the presence of the testcompound to a reference level, e.g., the level of the target mRNA in theabsence of the test compound, wherein a difference in the level oftarget mRNA relative to the reference level of target mRNA indicateswhether the pathogen is sensitive or resistant to the test compound.

In one embodiment, the pathogen is known, e.g., an identified pathogen.In some embodiments, the methods determine the drug sensitivity of anunknown pathogen, e.g., a yet to be identified pathogen.

In some embodiments, the sample comprising the pathogen is contactedwith two or more test compounds, e.g., simultaneously or in the samesample, e.g., contacted with known or potential treatment compounds,e.g., antibiotics, antifungals, antivirals, and antiparasitics. A numberof these compounds are known in the art, e.g., isoniazid, rifampicin,pyrazinamide, ethambutol streptomycin, amikacin, kanamycin, capreomycin,viomycin, enviomycin, ciprofloxacin, levofloxacin, moxifloxacin,ethionamide, prothionamide, cycloserine, p-aminosalicylic acid,rifabutin, clarithromycin, linezolid, thioacetazone, thioridazine,arginine, vitamin D, R207910, ofloxacin, novobiocin, tetracycline,merepenem, gentamicin, neomycin, netilmicin, streptomycin, tobramycin,paromomycin, geldanamycin, herbimycin, loracarbef, ertapenem, doripenem,imipenem/cilastatin, meropenem, cefadroxil, cefazolin, cefalotin,cefalexin, cefaclor, cefamandole, cefoxitin, cefprozil, cefuroxime,cefixime, cefdinir, cefditoren, cefoperazone, cefotaxime, cefpodoxime,ceftazidime, ceftibuten, ceftizoxime, ceftriaxone, cefepime,ceftobiprole, teicoplanin, vancomycin, azithromycin, dirithromycin,erythromycin, roxithromycin, troleandomycin, telithromycin,spectinomycin, aztreonam, amoxicillin, ampicillin, azlocillin,carbenicillin, cloxacillin, dicloxacillin, flucloxacillin, mezlocillin,methicillin, nafcillin, oxacillin, penicillin, piperacillin,ticarcillin, bacitracin, colistin, polymyxin B, enoxacin, gatifloxacin,lomefloxacin, norfloxacin, trovafloxacin, grepafloxacin, sparfloxacin,mafenide, prontosil, sulfacetamide, sulfamethizole, sulfanilimide,sulfasalazine, sulfisoxazole, trimethoprim,trimethoprim-sulfamethoxazole (co-trimoxazole), demeclocycline,doxycycline, minocycline, oxytetracycline, arsphenamine,chloramphenicol, clindamycin, lincomycin, ethambutol, fosfomycin,fusidic acid, furazolidone, metronidazole, mupirocin, nitrofurantoin,platensimycin, quinupristin/dalfopristin, rifampin, thiamphenicol,tinidazole, cephalosporin, teicoplatin, augmentin, cephalexin,rifamycin, rifaximin, cephamandole, ketoconazole, latamoxef, orcefmenoxime.

In some embodiments, the sample is contacted with the compound for lessthan four hours, e.g., less than three hours, less than two hours, lessthan one hour, less than 30 minutes, less than 20 minutes, less than 10minutes, less than five minutes, less than two minutes, less than oneminute.

In another aspect, the invention features methods of identifying aninfectious disease pathogen, e.g., a bacterium, fungus, virus, orparasite, e.g., Mycobacterium tuberculosis, e.g., detecting the presenceof the pathogen in a sample, e.g., a clinical sample. The methodsinclude:

providing a test sample from a subject suspected of being infected witha pathogen;

treating the test sample under conditions that release messengerribonucleic acid (mRNA);

exposing the test sample to a plurality of nucleic acid probes,comprising a plurality of subsets of probes, wherein each subsetcomprises one or more probes that bind specifically to a target mRNAthat uniquely identifies a pathogen, wherein the exposure occurs for atime and under conditions in which binding between the probe and thetarget mRNA can occur; and

determining a level of binding between the probe and target mRNA,thereby determining a level of target mRNA. An increase in the targetmRNA of the test sample, relative to a reference sample, indicates theidentity of the pathogen in the test sample.

In some embodiments, the methods identify an infectious disease pathogenin or from a sample that is or comprises sputum, blood, urine, stool,joint fluid, cerebrospinal fluid, and cervical/vaginal swab. Suchsamples may include a plurality of other organisms (e.g., one or morenon-disease causing bacteria, fungi, viruses, or parasites) orpathogens. In some embodiments, the sample is a clinical sample, e.g., asample from a patient or person who is or may be undergoing a medicaltreatment by a health care provider.

In some embodiments of the invention, the one or more nucleic acidprobes are selected from Table 2.

In some embodiments, the mRNA is crude, e.g., not purified, beforecontact with the probes and/or does not include amplifying the mRNA,e.g., to produce cDNA.

In some embodiments, the methods comprise lysing the cellsenzymatically, chemically, and/or mechanically.

In some embodiments, the methods comprise use of a microfluidic device.

In some embodiments, the methods are used to monitor pathogen infection,e.g., incidence, prevalence, for public health surveillance of anoutbreak of a pathogen, e.g., a sudden rise in numbers of a pathogenwithin a particular area.

The methods described herein are effective wherein the pathogen is in asample from a subject, including humans and animals, such as laboratoryanimals, e.g., mice, rats, rabbits, or monkeys, or domesticated and farmanimals, e.g., cats, dogs, goats, sheep, pigs, cows, horses, and birds,e.g., chickens.

In some embodiments, the methods further feature determining and/orselecting a treatment for the subject and optionally administering thetreatment to the subject, based on the outcome of an assay as describedherein.

In another general aspect, the invention features methods of selecting atreatment for a subject. The methods include:

optionally identifying an infectious disease pathogen (e.g., detectingthe presence and/or identity of a specific pathogen in a sample), e.g.,using a method described herein;

determining the drug sensitivity of the pathogen using the methodsdescribed herein; and

selecting a drug to which the pathogen is sensitive for use in treatingthe subject.

In yet another aspect, the invention provides methods for monitoring aninfection with a pathogen in a subject. The methods include:

obtaining a first sample comprising the pathogen at a first time;

determining the drug sensitivity of the pathogen in the first sampleusing the method described herein;

optionally selecting a treatment to which the pathogen is sensitive andadministering the selected treatment to the subject;

obtaining a second sample comprising the pathogen at a second time;

determining the drug sensitivity of the pathogen in the second sampleusing the method described herein; and

comparing the drug sensitivity of the pathogen in the first sample andthe second sample, thereby monitoring the infection in the subject.

In some embodiments of the methods described herein, the subject isimmune compromised.

In some embodiments of the methods described herein, the methods includeselecting a treatment to which the pathogen is sensitive andadministering the selected treatment to the subject, and a change in thedrug sensitivity of the pathogen indicates that the pathogen is or isbecoming resistant to the treatment, e.g., the methods includedetermining the drug sensitivity of the pathogen to the treatment beingadministered.

In some embodiments, a change in the drug sensitivity of the pathogenindicates that the pathogen is or is becoming resistant to thetreatment, and the method further comprises administering a differenttreatment to the subject.

In yet another aspect, the invention features methods of monitoring aninfection with a pathogen in a population of subjects. The methodsinclude:

obtaining a first plurality of samples from subjects in the populationat a first time;

determining the drug sensitivity of pathogens in the first plurality ofsamples using the method described herein, and optionally identifying aninfectious disease pathogen in the first plurality of samples using themethod described herein;

optionally administering a treatment to the subjects;

obtaining a second plurality of samples from subjects in the populationat a second time;

determining the drug sensitivity of pathogens in the second plurality ofsamples using the method described herein, and optionally identifying aninfectious disease pathogen in the first plurality of samples using themethod described herein;

comparing the drug sensitivity of the pathogens, and optionally theidentity of the pathogens, in the first plurality of samples and thesecond plurality of samples, thereby monitoring the infection in thepopulation of subject.

In yet another aspect, a plurality of polynucleotides bound to a solidsupport are provided. Each polynucleotide of the plurality selectivelyhybridizes to one or more genes from Table 2. In some embodiments, theplurality of polynucleotides comprise SEQ ID NOs:1-227, and anycombination thereof,

“Infectious diseases” also known as communicable diseases ortransmissible diseases, comprise clinically evident illness (i.e.,characteristic medical signs and/or symptoms of disease) resulting fromthe infection, presence, and growth of pathogenic biological agents in asubject (Ryan and Ray (eds.) (2004). Sherris Medical Microbiology (4thed.). McGraw Hill). A diagnosis of an infectious disease can confirmedby a physician through, e.g., diagnostic tests (e.g., blood tests),chart review, and a review of clinical history. In certain cases,infectious diseases may be asymptomatic for some or all of their course.Infectious pathogens can include viruses, bacteria, fungi, protozoa,multicellular parasites, and prions. One of skill in the art wouldrecognize that transmission of a pathogen can occur through differentroutes, including without exception physical contact, contaminated food,body fluids, objects, airborne inhalation, and through vector organisms.Infectious diseases that are especially infective are sometimes referredto as contagious and can be transmitted by contact with an ill person ortheir secretions.

As used herein, the term “gene” refers to a DNA sequence in a chromosomethat codes for a product (either RNA or its translation product, apolypeptide). A gene contains a coding region and includes regionspreceding and following the coding region (termed respectively “leader”and “trailer”). The coding region is comprised of a plurality of codingsegments (“exons”) and intervening sequences (“introns”) betweenindividual coding segments.

The term “probe” as used herein refers to an oligonucleotide that bindsspecifically to a target mRNA. A probe can be single stranded at thetime of hybridization to a target.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Methods and materials aredescribed herein for use in the present invention; other, suitablemethods and materials known in the art can also be used. The materials,methods, and examples are illustrative only and not intended to belimiting. All publications, patent applications, patents, sequences,database entries, and other references mentioned herein are incorporatedby reference in their entirety. In case of conflict, the presentspecification, including definitions, will control.

Other features and advantages of the invention will be apparent from thefollowing detailed description and figures, and from the claims.

DESCRIPTION OF DRAWINGS

FIGS. 1A to 1D are a flowchart illustrating an exemplary method toquantify mRNA molecules in a sample using NanoString™ (directmultiplexed measurement of gene expression with color-coded probe pairs)technology. FIG. 1A. Two molecular probes corresponding to each mRNA ofinterest are added to crude sample lysate. The capture probe consists ofa 50 bp oligomer complementary to a given mRNA molecule, conjugated tobiotin. The reporter probe consists of a different 50 bp oligomercomplementary to a different part of the same mRNA molecule, conjugatedto a fluorescent tag. Each tag uniquely identifies a given mRNAmolecule. The capture and reporter probes hybridize to theircorresponding mRNA molecules within the lysate. FIG. 1B. Excess reporteris removed by bead purification that hybridizes to a handle on eacholigomer, leaving only the hybridized mRNA complexes. FIG. 1C. The mRNAcomplexes are immobilized and aligned on a surface. The mRNA complexesare captured by the biotin-conjugated captures probes onto astrepavidin-coated surface. An electric field is applied to align thecomplexes all in the same direction on the surface. FIG. 1D. Surface isimaged and codes counted. The mRNA complexes are microscopically imagedand the aligned reporter tags can be counted, thus providing aquantitative measure of mRNA molecules. (Images obtained fromnanostring.com).

FIGS. 2A to 2F are a panel of figures showing diagnosis of a geneexpression signature of drug resistance. FIG. 2A. Sample from a patient,e.g., sputum. FIG. 2B. Induction of expression program to distinguishdrug sensitive and resistant strains. Sample is partitioned and exposedto different drugs to induce an expression program depending on whetherthe strain is drug resistant or sensitive. FIG. 2C. Bar-coded probeshybridize to mRNA molecules. Cells are lysed and probes added to thecrude sample. FIG. 2D. mRNA complexes are captured and aligned. FIG. 2E.Complexes are imaged and counted. FIG. 2F. Analysis of signatures. Themeasured mRNA levels will be normalized and compared to the no drugcontrol and drug sensitive and resistant standards to define aresistance profile across all drugs.

FIG. 3 is a bar graph showing positive identification of E. coliclinical isolates. Using probes designed to six E. coli genes (ftsQ,murC, opgG, putP, secA, and uup), four clinical isolates were positivelyidentified as E. coli. Each value represents average and standarddeviation of 4 to 6 replicates.

FIG. 4 is a bar graph showing positive identification of Pseudomonasaeruginosa clinical isolates. Using probes designed to five P.aeruginosa genes (proA, sltB1, nadD, dacC, and lipB), two clinicalisolates were positively identified as P. aeruginosa.

FIG. 5 is a bar graph showing positive identification of a Klebsiellapneumoniae clinical isolate. Using probes designed to five K. pneumoniaegenes (lrp, ycbK, clpS, ihfB, mraW) a clinical isolate was positivelyidentified.

FIG. 6 is a bar graph showing positive identification of S. aureusclinical isolates. Using probes designed to three S. aureus genes (proC,rpoB, and fabD), four clinical isolates were positively identified.

FIG. 7 is a panel of three bar graphs showing pathogen identificationusing pathogen specific probes.

FIG. 8 is a panel of three bar graphs showing pathogen identificationsensitivity.

FIGS. 9A and 9B are panels of three bar graphs showing pathogenidentification from simulated clinical samples.

FIG. 10 is a panel of two bar graphs showing identification of twoclinical isolates of P. aeruginosa.

FIG. 11 is a bar graph showing the identification of fluoroquinoloneresistance in E. coli.

FIG. 12 is a bar graph showing the identification of aminoglycosideresistance in E. coli.

FIG. 13 is a bar graph showing the identification of methicillinresistance in S. aureus.

FIG. 14 is a bar graph showing the identification of vancomycinresistance in Enterococcus.

FIG. 15 is a panel of four bar graphs showing drug-specific geneinduction in drug-sensitive M. tuberculosis.

FIG. 16 is panel of three scatter plots comparing isoniazid sensitiveand resistant TB strains. Each dot represents one of the 24 gene probes.The axes report number of transcripts as measured by digital geneexpression technology (NanoString™). Left—Comparison of expression inisoniazid resistant and isoniazid sensitive strains in the absence ofdrug treatment. Middle—Comparison of expression in drug treated vs. druguntreated isoniazid sensitive strain. Right—Comparison of expression indrug treated vs. drug untreated isoniazid resistant strain.

FIG. 17 is a panel of four bar graphs comparing the transcriptionalresponses of drug-sensitive and drug-resistant M. tuberculosis usingNanoString™. (A) Strain A50 (INH-R) was treated with INH (0.4 μg/ml) asdescribed herein. (B) The SM-R clone S10 was treated with 2 μg/mlstreptomycin.

FIG. 18 is bar graph showing differential gene induction in sensitivevs. resistant TB strain. The ratio of expression of each gene in INHsensitive (wt) cells treated with INH/untreated cells is divided by theexpression of each gene in INH resistant cells treated withINH/untreated cells.

FIG. 19 is a line graph showing the time course of induction ofINH-induced genes in M. tuberculosis. Isoniazid sensitive H37Rv wasexposed to 0.4 μg/ml INH (5×MIC), and RNA was prepared from 10 ml ofculture at 1, 2, and 5 hours. qRT-PCR was then used to quantify theabundance of transcripts to kasA, kasB, and sigA, Levels are normalizedto sigA and compared to t=0.

FIG. 20 is an exemplary work flow for detecting expression signatures.Because the actual physiologic state of bacilli in sputum is unknown,both replicating and non-replicating bacteria are modeled in processdevelopment. H37Rv grown in axenic culture (either in rich 7H9/OADC/SDSmedia or starved in 7H9/tyloxapol) represent bacilli in sputum in theseexperiments. The bacilli are pulsed for some time t₁ with exposure torich media to stimulate resuscitation from a dormant state and to activetranscription. The optimal t₁ is determined experimentally. The bacilliare then pulsed for some time t₂ with exposure to drug to elicit atranscriptional response. The optimal t₂ is determined experimentally.Finally, all samples are processed and analyzed by expression profilingand confirmed by quantitative RT-PCR.

FIG. 21 is an exemplary method to compare expression ratios of genes todistinguish drug sensitive and resistant bacilli. Using quantitativeRT-PCR, mRNA levels are measured for genes that are candidates forinclusion in an expression signature. The mRNA levels of a gene ofinterest are measured in a sample designated “experimental (exp)” (i.e.,clinical isolate) in the presence of drug (induced-drug) and the absenceof drug (uninduced-no drug). The mRNA levels of a standard housekeepinggene are also measured in the presence (housekeeping-drug) and absence(housekeeping-no drug) of drug. The ratio of the levels of the gene ofinterest and the housekeeping gene allow for normalization of expressionin the presence of drug (A) and in the absence of drug (B). It isanticipated that for some drug sensitive strains, A>B and for drugresistant strains, A=B. Finally, the same corresponding ratios aregenerated for control strains (C and D) that are known to be drugsensitive and drug resistant. These control values act as standards forthe comparison of experimental ratios obtained from unknown strains.

FIG. 22 is a panel of bar and scatter plots showing positiveidentification of bacterial species directly from culture or patientspecimens. Bacterial samples were analyzed with NanoString™ probesdesigned to detect species-specific transcripts. Y-axis: transcript rawcounts; X-axis: gene name. Probes specific for E. coli (black), K.pneumoniae (white), P. aeruginosa (grey). Error bars reflect thestandard deviation of two biological replicates. (A) Detection fromculture of Gram-negative bacteria. (B) Detection within mixed culture(Providencia stuartii, Proteus mirabilis, Serratia marcescens,Enterobacter aerogenes, Enterobacter cloacae, Morganella morganii,Klebsiella oxytoca, Citrobacter freundii). (C) Genus- andspecies-specific detection of mycobacteria in culture. M. tuberculosis(Mtb), M. avium subsp. intracellulare (MAI), M. paratuberculosis(Mpara), and M. marinum (Mmar). Genus-wide probes (grey), M.tuberculosis-specific probes (black). (D) Detection of E. coli directlyfrom clinical urine specimens. (E) Statistical determination of identityof E. coli samples in comparison with non-E. coli samples. Counts foreach probe were averaged, log transformed and summed. (F) Detection ofmecA mRNA, which confers resistance to methicillin in Staphylococci, andvanA mRNA, which confers resistance to vancomycin in Enterococci. Eachpoint represents a different clinical isolate.

FIG. 23 is a panel of seven bar graphs showing RNA expression signaturesthat distinguish sensitive from resistant bacteria upon antibioticexposure. Sensitive or resistant bacterial strains were grown to logphase, briefly exposed to antibiotic, lysed, and analyzed usingNanoString™ probe-sets designed to quantify transcripts that change inresponse to antibiotic exposure. Raw counts were normalized to the meanof all probes for a sample, and fold induction was determined bycomparing drug-exposed to unexposed samples. Y-axis: fold-change;X-axis: gene name. Signatures for susceptible strains (black; top panel)or resistant strains (grey; bottom panel) upon exposure to (A) E. coli:ciprofloxacin (CIP), ampicillin (AMP), or gentamicin (GM), (B) P.aeruginosa: ciprofloxacin, and (C) M. tuberculosis: isoniazid (INH),streptomycin (SM), or ciprofloxacin (CIP). Each strain was tested induplicate; error bars represent standard deviation of two biologicalreplicates of one representative strain. See Table 6 for a full list ofstrains tested.

FIG. 24 is a panel of three scatter plots showing statistical separationof antibiotic-resistant and sensitive bacterial strains using meansquared distance of the induction levels of expression signatures. Meansquared distance (MSD) is represented as Z-scores showing deviation ofeach tested strain from the mean signal for susceptible strains exposedto antibiotic. Susceptible strains: open diamonds; resistant strains:solid diamonds. Dashed line: Z=3.09 (p=0.001) (A) E. coli clinicalisolates. Each point represents 2 to 4 biological replicates of onestrain. (B and C) Expression-signature response to antibiotic exposureis independent of resistance mechanism. (B) E. coli. Parent strain J53and derivatives containing either a chromosomal fluoroquinoloneresistance-conferring mutation in gyrA or plasmid-mediated quinoloneresistance determinants (aac(6′)-Ib, qnrB, or oqxAB) were exposed tociprofloxacin, then analyzed as above. Error bars represent standarddeviation of four biological replicates. (C) M tuberculosis.Isoniazid-sensitive and high- or low-level resistant strains wereexposed to isoniazid. At 1 μg/mL, the low-level INH-resistant inhAdisplays a susceptible signature, but at 0.2 μg/mL, it shows a resistantsignature.

FIG. 25 is a panel of five bar graphs depicting detection of viruses andparasites. Cells were lysed, pooled probe sets added, and sampleshybridized according to standard NanoString™ protocols. (A) Candidaalbicans detected from axenic culture. (B) HIV-1. Detection from PBMClysates with probes designed to HIV-1 gag and rev. (C) Influenza A.Detection of PR8 influenza virus in 293T cell lysates with probesdesigned to matrix proteins 1 and 2. (D) HSV-1 and HSV-2. Detection ofHSV-2 strain 186 Syn+ in HeLa cell lysates with probes designed to HSV-2glycoprotein G. There was little cross-hybridization of the HSV-2specific probes with HSV-1 even at high MOI. (E) Plasmodium falciparum.Detection of P. falciparum strain 3D7 from red blood cells harvested atthe indicated levels of parasitemia. Probes were designed to theindicated blood stage for P. falciparum.

FIG. 26 is a panel of three scatter plots showing organismidentification of clinical isolates. Bacterial cultures were lysed andprobes that were designed to detect species-specific transcripts wereadded, hybridized, and detected by standard NanoString™ protocol. Apooled probe-set containing probes that identify E. coli, K. pneumoniae,or P. aeruginosa were used in A and B. In C, species-specific probes forM. tuberculosis were among a larger set of probes against microbialpathogens. The left Y-axis shows the sum of the log-transformed countsfrom 1-5 independent transcripts for each organism and X-axis indicatesthe species tested. The dashed line delineates a p value of 0.001 basedon the number of standard deviations that the score of a given samplefalls from the mean of the control (“non-organism”) samples.“Non-organism” samples indicate samples tested that contained otherbacterial organisms but where the defined organism was known to beabsent. For (C), non-organism samples were non-tuberculous mycobacteriaincluding M. intracellulare, M. paratuberculosis, M. abscessus, M.marinum, M. gordonae, and M. fortuitum. Numbers of strains and clinicalisolates tested are shown in Table 4 and genes used for pathogenidentification (for which 50 nt probes were designed) are listed inTable 5.

FIG. 27 depicts the mean square distance (MSD) comparison of gentamicin(left panel) or ampicillin (left panel) sensitive and resistant E. colistrains. The Y axis shows the Z score of the MSD of each sample relativeto the centroid of the response of known sensitive strains. The dottedline delineates Z=3.09, which corresponds to a p value of 0.001.

FIG. 28 is a scatter plot showing mean square distance comparison ofciprofloxacin sensitive and resistant P. aeruginosa strains. The Y axisshows the Z score of the MSD of each sample relative to the centroid ofthe response of known sensitive strains.

FIG. 29 is a panel of two scatter plots showing mean square distancecomparison of streptomycin (SM) or ciprofloxacin (CIP) sensitive andresistant M. tuberculosis strains. The Y axis shows the Z score of theMSD of each sample relative to the centroid of the response of knownsensitive strains.

FIG. 30 is a bar graph showing positive identification of S. aureusisolates. Using probes designed to five S. aureus genes (ileS, ppnK,pyrB, rocD, and uvrC), three S. aureus isolates were positivelyidentified.

FIG. 31 is a bar graph showing positive identification ofStenotrophomonas maltophilia isolates. Using probes designed to six S.maltophilia genes (clpP, dnaK, purC, purF, sdhA, and secD), threeisolates were positively identified as S. maltophilia.

DETAILED DESCRIPTION

Described herein are rapid, highly sensitive, phenotypic-based methodsfor both identifying a pathogen, e.g., bacterium, fungus, virus, andparasite, and its drug resistance pattern based on transcriptionalexpression profile signatures. Sensitive and resistant pathogens respondvery differently to drug exposure with one of the earliest, most rapidresponses reflected in alterations in their respective expressionprofiles. Digital gene expression with molecular barcodes can be used todetect these early transcriptional responses to drug exposure todistinguish drug sensitive and resistant pathogens in a rapid mannerthat requires no enzymology or molecular biology. The invention isapplicable to a broad range of microbial pathogens in a variety ofclinical samples and can be used in conjunction with current diagnostictools or independently. The methods will be described primarily for usewith tuberculosis (“TB;” Mycobacterium tuberculosis), although it willbe understood by skilled practitioners that they may be adapted for usewith other pathogens and their associated clinical syndromes (e.g., aslisted in Table 1).

The diagnosis and the identification of drug resistance is especiallychallenging regarding TB due to the extremely slow growth of TB that isrequired for culture testing even using the more rapid“microscopic-observation drug-susceptibility” (MODS) culture method,phage-delivered reporters, or colorimetric indicators. An alternativeapproach to determining drug resistance is based on defining thegenotype of the infecting pathogen relative to known resistanceconferring mutations, however, this approach requires a fairlycomprehensive identification of all resistance-conferring singlenucleotide polymorphisms (SNPs) in order for the test to have highsensitivity.

The methods described herein can be used, e.g., for identifying apathogen in a sample, e.g., a clinical sample, as well as determiningthe drug sensitivity of a pathogen based on expression profilesignatures of the pathogen. One of the earliest, most rapid responsesthat can be used to distinguish drug sensitive and resistant pathogensis their respective transcriptional profile upon exposure to a drug ofinterest. Pathogens respond very differently to drug exposure dependingon whether they are sensitive or resistant to that particular drug. Forexample, in some cases drug sensitive or drug resistant bacteria willrespond within minutes to hours to drug exposure by up- anddown-regulating genes, perhaps attempting to overcome the drug as wellas the more non-specific stresses that follow while resistant bacteriahave no such response. This rapid response is in contrast to the longertime that is required by a compound to kill or inhibit growth of apathogen. Detecting death or growth inhibition of a pathogen in anefficient manner from clinical samples represents an even greaterchallenge. Digital gene expression can be used, e.g., with molecularbarcodes, to detect these early transcriptional responses to drugexposure to distinguish drug sensitive and resistant pathogens in arapid manner that requires no enzymology or molecular biology, and thuscan be performed directly on crude clinical samples collected frompatients. This readout is phenotypic and thus requires no comprehensivedefinition of SNPs accounting for, e.g., TB drug resistance. Describedherein are a set of genes that will provide high specificity for apathogen, e.g., TB bacillus, and for distinguishing sensitive andresistant pathogens. Based on the selection of genes that constitute theexpression signature distinguishing sensitive and resistant pathogens,the sensitivity of the detection limit is optimized by choosingtranscripts that are abundantly induced, and thus not limited solely bythe number of pathogens within a clinical sample. The size of this setis determined to minimize the numbers of genes required. Thus, thecurrent invention can be used as a highly sensitive, phenotypic test todiagnose a pathogen with its accompanying resistance pattern that israpid (e.g., a few hours), sensitive, and specific. This test cantransform the care of patients infected with a pathogen and is acost-effective, point-of-care diagnostic for, e.g., TB endemic regionsof the world.

The present methods allow the detection of nucleic acid signatures,specifically RNA levels, directly from crude cellular samples with ahigh degree of sensitivity and specificity. This technology can be usedto identify TB and determine drug sensitivity patterns throughmeasurement of distinct expression signatures with a high degree ofsensitivity and with rapid, simple processing directly from clinicalsamples, e.g. sputum, urine, blood, or feces; the technology is alsoapplicable in other tissues such as lymph nodes. High sensitivity can beattained by detecting mRNA rather than DNA, since a single cell cancarry many more copies of mRNA per cell (>10³) compared to a singlegenomic DNA copy (which typically requires amplification for detection),and by the high inherent sensitivity of the technology (detects <2000copies mRNA). The rapid, simple sample processing is possible due to thelack of enzymology and molecular biology required for detection of mRNAmolecules; instead, in some embodiments, the methods make use ofhybridization of bar-coded probes to the mRNA molecules in crude lysatesfollowed by direct visualization (e.g., as illustrated in FIG. 1).Because hybridization is used in these embodiments, mRNA can be detecteddirectly without any purification step from crude cell lysates, fixedtissue samples, and samples containing guanidinium isothiocyanate,polyacrylamide, and Trizol®. Crude mRNA samples can be obtained frombiological fluids or solids, e.g., sputum, blood, urine, stool, jointfluid, cerebrospinal fluid, cervical/vaginal swab, biliary fluid,pleural fluid, peritoneal fluid, or pericardial fluid; or tissue biopsysamples, e.g., from bone biopsy, liver biopsy, lung biopsy, brainbiopsy, lymph node biopsy, esophageal biopsy, colonic biopsy, gastricbiopsy, small bowel biopsy, myocardial biopsy, skin biopsy, and sinusbiopsy can also be used.

RNA Extraction

RNA can be extracted from cells in a sample, e.g., a pathogen cell orclinical sample, by treating the sample enzymatically, chemically, ormechanically to lyse cells in the sample and release mRNA. It will beunderstood by skilled practitioners that other disruption methods may beused in the process.

The use of enzymatic methods to remove cell walls is well-established inthe art. The enzymes are generally commercially available and, in mostcases, were originally isolated from biological sources. Enzymescommonly used include lysozyme, lysostaphin, zymolase, mutanolysin,glycanases, proteases, and mannose.

Chemicals, e.g., detergents, disrupt the lipid barrier surrounding cellsby disrupting lipid-lipid, lipid-protein and protein-proteininteractions. The ideal detergent for cell lysis depends on cell typeand source. Bacteria and yeast have differing requirements for optimallysis due to the nature of their cell wall. In general, nonionic andzwitterionic detergents are milder. The Triton X series of nonionicdetergents and 3-[(3-Cholamidopropyl)dimethylammonio]-1-propanesulfonate(CHAPS), a zwitterionic detergent, are commonly used for these purposes.In contrast, ionic detergents are strong solubilizing agents and tend todenature proteins, thereby destroying protein activity and function.SDS, an ionic detergent that binds to and denatures proteins, is usedextensively in the art to disrupt cells.

Physical disruption of cells may entail sonication, French press,electroporation, or a microfluidic device comprising fabricatedstructures can be used to mechanically disrupt a cell. These methods areknown in the art.

Digital Gene Expression with Molecular Barcodes

A flow diagram is shown in FIG. 1 of an exemplary procedure to identifya pathogen based on its gene expression profile. Oligonucleotide probesto identify each pathogen of interest were selected by comparing thecoding sequences from the pathogen of interest to all gene sequences inother organisms by BLAST software. Only probes of about 50 nucleotides,e.g., 80 nucleotides, 70 nucleotides, 60 nucleotides, 40 nucleotides, 30nucleotides, and 20 nucleotides, with a perfect match to the pathogen ofinterest, but no match of >50% to any other organism were selected. Twoprobes corresponding to each mRNA of interest and within 100 base pairsof each other are selected.

Two molecular probes are added to a crude sample lysate containing mRNAmolecules. A capture probe comprises 50 nucleotides complementary to agiven mRNA molecule, and can be conjugated to biotin. A reporter probecomprises a different 50 nucleotides complementary to a different partof the same mRNA molecule, and can be conjugated to a reporter molecule,e.g., a fluorescent tag or quantum dot. Each reporter molecule uniquelyidentifies a given mRNA molecule. The capture and reporter probeshybridize to their corresponding mRNA molecules within the lysate.Excess reporter is removed by bead purification that hybridizes to ahandle on each oligomer, leaving only the hybridized mRNA complexes. ThemRNA complexes can be captured and immobilized on a surface, e.g., astreptavidin-coated surface. An electric field can be applied to alignthe complexes all in the same direction on the surface before thesurface is microscopically imaged.

The reporter molecules can be counted to provide a quantitative measureof mRNA molecules. A commercially available nCounter™ Analysis System(NanoString, Seattle, Wash.) can be used in the procedure. However, itwill be understood by skilled practitioners that other systems may beused in the process. For example, rather than bar codes the probes canbe labeled with quantum dots; see, e.g., Sapsford et al., “Biosensingwith luminescent semiconductor quantum dots.” Sensors 6(8): 925-953(2006); Stavis et al., “Single molecule studies of quantum dotconjugates in a submicrometer fluidic channel.” Lab on a Chip 5(3):337-343 (2005); and Liang et al., “An oligonucleotide microarray formicroRNA expression analysis based on labeling RNA with quantum dot andnanogold probe.” Nucleic Acids Research 33(2): ell (2005).

In some embodiments, microfluidic (e.g., “lab-on-a-chip”) devices can beused in the present methods for detection and quantification of mRNA ina sample. Such devices have been successfully used for microfluidic flowcytometry, continuous size-based separation, and chromatographicseparation. In particular, such devices can be used for the detection ofspecific target mRNA in crude samples as described herein. A variety ofapproaches may be used to detect changes in levels of specific mRNAs.Accordingly, such microfluidic chip technology may be used in diagnosticand prognostic devices for use in the methods described herein. Forexamples, see, e.g., Stavis et al., Lab on a Chip 5(3): 337-343 (2005);Hong et al., Nat. Biotechnol. 22(4):435-439 (2004); Wang et al.,Biosensors and Bioelectronics 22(5): 582-588 (2006); Carlo et al., Labon a Chip 3(4):287-291 (2003); Lion et al., Electrophoresis 24 213533-3562 (2003); Fortier et al., Anal. Chem., 77(6):1631-1640 (2005);U.S. Patent Publication No. 2009/0082552; and U.S. Pat. No. 7,611,834.Also included in the present application are microfluidic devicescomprising binding moieties, e.g., antibodies or antigen-bindingfragments thereof that bind specifically to the pathogens as describedherein.

These microfluidic devices can incorporate laser excitation of labeledquantum dots and other reporter molecules. The devices can alsoincorporate the detection of the resulting emission through a variety ofdetection mechanisms including visible light and a variety of digitalimaging sensor methods including charge-coupled device based cameras.These devices can also incorporate image processing and analysiscapabilities to translate the resulting raw signals and data intodiagnostic information.

Rapid, Phenotypic Diagnosis of Pathogen Identity and Pathogen DrugResistance Using Expression Signatures

This technology can be applied to obtain a rapid determination ofidentity or drug resistance of a pathogen.

A pathogen can be identified in a sample based on detection of uniquegenes. Thus, for example, a sputum sample may be obtained from a subjectwho has symptoms associated with a respiratory disease such as pneumoniaor bronchitis, and an assay is performed to determine which disease ispresent and what pathogen is the cause of that disease (see, e.g., Table1). Urine samples may be obtained to diagnose cystitis, pyelonephritis,or prostatitis (see, e.g., Table 1). A skilled practitioner willappreciate that a particular type of sample can be obtained and assayeddepending on the nature of the symptoms exhibited by the patient and thedifferential diagnosis thereof. Specific genes for identifying eachorganism can be identified by methods described herein; exemplary genesfor identifying certain pathogens are included in Table 2.

The principle for greatly accelerated resistance testing is based ondetecting the differences in transcriptional response that occur betweendrug sensitive and resistant strains of a pathogen upon exposure to aparticular drug of interest. These transcriptional profiles are theearliest phenotypic response to drug exposure that can be measured andthey can be detected long before bacillary death upon drug exposure.This transcription-based approach also carries the distinct advantageover genotype-based approaches in that it measures direct response ofthe pathogen to drug exposure rather than a surrogate SNP.

In some embodiments, the test can be performed as described in FIG. 2. Asample, e.g., a sputum sample from a patient with TB, is partitionedinto several smaller sub-samples. The different sub-samples are exposedto either no drug or different, known or potential drugs (e.g., in thecase of a TB sample, isoniazid, rifampin, ethambutol, moxifloxacin,streptomycin) for a determined period of time (e.g., less than fourhours, less than three hours, less than two hours, less than one hour,less than 30 minutes, less than 20 minutes, less than 10 minutes, lessthan five minutes, less than two minutes, less than one minute), duringwhich an expression profile is induced in drug sensitive strains thatdistinguishes it from drug resistant strains. The TB bacilli in thesub-samples are then lysed, the bar-coded molecular probes added forhybridization, and the sub-samples analyzed after immobilization andimaging. The set of transcriptional data is then analyzed to determineresistance to a panel of drugs based on expression responses for drugsensitive and drug resistant strains of TB. Thus, an expressionsignature to uniquely identify TB and its response to individualantibiotics can be determined, a probe set for the application ofdigital gene expression created, and sample processing and collectionmethods optimized.

Two issues that should be taken into account in defining the expressionsignatures and optimizing the transcriptional signal are: 1. thecurrently undefined metabolic state of the bacilli in sputum since thecells may be in either a replicating or non-replicating state, and 2.the possibility that the TB bacilli in collected sputum have beenpre-exposed to antibiotics (i.e., the patient has already been treatedempirically with antibiotics).

In some embodiments, the methods of identifying a pathogen and themethods of determining drug sensitivity are performed concurrently,e.g., on the same sample, in the same microarray or microfluidic device,or subsequently, e.g., once the identity of the pathogen has beendetermined, the appropriate assay for drug sensitivity is selected andperformed.

An exemplary set of genes and probes useful in the methods describedherein is provided in Table 2 submitted herewith.

Methods of Treatment

The methods described herein include, without limitation, methods forthe treatment of disorders, e.g., disorders listed in Table 1.Generally, the methods include using a method described herein toidentify a pathogen in a sample from a subject, or identify a drug (ordrugs) to which a pathogen in a subject is sensitive, and administeringa therapeutically effective amount of therapeutic compound thatneutralizes the pathogen to a subject who is in need of, or who has beendetermined to be in need of, such treatment. As used in this context, to“treat” means to ameliorate at least one symptom of the disorderassociated with one of the disorders listed in Table 1. For example, themethods include the treatment of TB, which often results in a cough,chest pain, fever, fatigue, unintended weight loss, loss of appetite,chills and night sweats, thus, a treatment can result in a reduction ofthese symptoms. Clinical symptoms of the other diseases are well knownin the art.

An “effective amount” is an amount sufficient to effect beneficial ordesired results. For example, a therapeutic amount is one that achievesthe desired therapeutic effect. This amount can be the same or differentfrom a prophylactically effective amount, which is an amount necessaryto prevent onset of disease or disease symptoms. An effective amount canbe administered in one or more administrations, applications or dosages.A therapeutically effective amount of a composition depends on thecomposition selected. The compositions can be administered from one ormore times per day to one or more times per week, including once everyother day. The compositions can also be administered from one or moretimes per month to one or more times per year. The skilled artisan willappreciate that certain factors may influence the dosage and timingrequired to effectively treat a subject, including but not limited tothe severity of the disease or disorder, previous treatments, thegeneral health and/or age of the subject, and other diseases present.Moreover, treatment of a subject with a therapeutically effective amountof the compositions described herein can include a single treatment or aseries of treatments.

Methods of Diagnosis

Included herein are methods for identifying a pathogen and/ordetermining its drug sensitivity. The methods include obtaining a samplefrom a subject, and evaluating the presence and/or drug sensitivity of apathogen in the sample, and comparing the presence and/or drugsensitivity with one or more references, e.g., a level in an unaffectedsubject or a wild type pathogen. The presence and/or level of a mRNA canbe evaluated using methods described herein and are known in the art,e.g., using quantitative immunoassay methods. In some embodiments, highthroughput methods, e.g., gene chips as are known in the art (see, e.g.,Ch. 12, Genomics, in Griffiths et al., Eds. Modern Genetic Analysis,1999,W. H. Freeman and Company; Ekins and Chu, Trends in

Biotechnology, 1999, 17:217-218; MacBeath and Schreiber, Science 2000,289(5485):1760-1763; Simpson, Proteins and Proteomics: A LaboratoryManual, Cold Spring Harbor Laboratory Press; 2002; Hardiman, MicroarraysMethods and Applications: Nuts & Bolts, DNA Press, 2003), can be used todetect the presence and/or level of mRNA.

In some embodiments, the sample includes biological fluids or solids,e.g., sputum, blood, urine, stool, joint fluid, cerebrospinal fluid,cervical/vaginal swab, biliary fluid, pleural fluid, peritoneal fluid,or pericardial fluid; or tissue biopsy samples, e.g., from a bonebiopsy, liver biopsy, lung biopsy, brain biopsy, lymph node biopsy,esophageal biopsy, colonic biopsy, gastric biopsy, small bowel biopsy,myocardial biopsy, skin biopsy, and sinus biopsy. In some embodiments,once it has been determined that a person has a pathogen, e.g., apathogen listed in Table 1, or has a drug-resistant pathogen, then atreatment, e.g., as known in the art or as described herein, can beadministered.

Kits

Also within the scope of the invention are kits comprising a probe thathybridizes with a region of gene as described herein and can be used todetect a pathogen described herein. The kit can include one or moreother elements including: instructions for use; and other reagents,e.g., a label, or an agent useful for attaching a label to the probe.Instructions for use can include instructions for diagnosticapplications of the probe for predicting response to treatment in amethod described herein. Other instructions can include instructions forattaching a label to the probe, instructions for performing analysiswith the probe, and/or instructions for obtaining a sample to beanalyzed from a subject. As discussed above, the kit can include alabel, e.g., a fluorophore, biotin, digoxygenin, and radioactiveisotopes such as ³²P and ³H. In some embodiments, the kit includes alabeled probe that hybridizes to a region of gene as described herein,e.g., a labeled probe as described herein.

The kit can also include one or more additional probes that hybridize tothe same gene or another gene or portion thereof that is associated witha pathogen. A kit that includes additional probes can further includelabels, e.g., one or more of the same or different labels for theprobes. In other embodiments, the additional probe or probes providedwith the kit can be a labeled probe or probes. When the kit furtherincludes one or more additional probe or probes, the kit can furtherprovide instructions for the use of the additional probe or probes.

Kits for use in self-testing can also be provided. For example, suchtest kits can include devices and instructions that a subject can use toobtain a sample, e.g., of sputum, buccal cells, or blood, without theaid of a health care provider. For example, buccal cells can be obtainedusing a buccal swab or brush, or using mouthwash.

Kits as provided herein can also include a mailer, e.g., a postage paidenvelope or mailing pack, that can be used to return the sample foranalysis, e.g., to a laboratory. The kit can include one or morecontainers for the sample, or the sample can be in a standard bloodcollection vial. The kit can also include one or more of an informedconsent form, a test requisition form, and instructions on how to usethe kit in a method described herein. Methods for using such kits arealso included herein. One or more of the forms, e.g., the testrequisition form, and the container holding the sample, can be coded,e.g., with a bar code, for identifying the subject who provided thesample.

In some embodiments, the kits can include one or more reagents forprocessing a sample. For example, a kit can include reagents forisolating mRNA from a sample. The kits can also, optionally, contain oneor more reagents for detectably-labeling an mRNA or mRNA amplicon, whichreagents can include, e.g., an enzyme such as a Klenow fragment of DNApolymerase, T4 polynucleotide kinase, one or more detectably-labeleddNTPs, or detectably-labeled gamma phosphate ATP (e.g., ³³P-ATP).

In some embodiments, the kits can include a software package foranalyzing the results of, e.g., a microarray analysis or expressionprofile.

The invention is further described in the following examples, which donot limit the scope of the invention described in the claims.

EXAMPLES Example 1 Pathogen Identification

Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, andStaphylococcus aureus. Unique coding sequences in Escherichia coli,Pseudomonas aeruginosa, Klebsiella pneumoniae, Staphylococcus aureus,and Enterococcus faecalis were identified (Table 2) and used topositively identify these organisms (FIGS. 3-6). Clinical isolates weregrown in LB media at 37° C. to log phase. Five microliters of eachculture were then added to 100 microliters of guanidinium isothiocyanatelysis buffer (RLT buffer, Qiagen) and vortexed for 5 seconds. Fourmicroliters of each lysate preparation were then used in the nCounter™System assay according to the manufacturer's standard protocol forlysates. Criteria for identification were counts for all five (for P.aeruginosa or K. pneumoniae) or six (for E. coli) organismidentification probes at least two-fold above the average background(the average of counts for all organism identification probes for theother two organisms). To compare between replicates, counts werenormalized to counts of proC. Using the organism identification probesdescribed in Table 2, four E. coli clinical isolates were correctlyidentified using probes designed to six E. coli genes (ftsQ, murC, opgG,putP, secA, and uup) (FIG. 3). Two clinical isolates were correctlyidentified as P. aeruginosa using probes designed to five P. aeruginosagenes (proA, sltB1, nadD, dacC, and lipB) as shown in FIG. 4. As shownin FIG. 5, probes designed to five K. pneumoniae genes (lrp, ycbK, clpS,ihfB, and mraW) positively identified a K. pneumoniae clinical isolate.Using probes designed to three S. aureus genes (proC, rpoB, and fabD),four clinical isolates were positively identified (FIG. 6). Cut-offcriteria for identification were that counts for rpoB and fabD are atleast two-fold above the average background (the average of counts forall organism identification probes for E. coli, P. aeruginosa, and K.pneumoniae).

On average, 4-5 sequences for each organism were included in the largerpool to obtain desired levels of specificity. Using this technology,each of these three organisms were detected, identified, anddistinguished in axenic culture and in a complex mixture including eightadditional Gram-negative pathogens by directly probing crude lysates(FIGS. 22A and 22B).

TB. Probes to Rv1641.1 and Rv3583c.1 detect highly abundant transcriptsin M. tuberculosis (reference 8) and will detect orthologous transcriptsin M. avium, and M. avium subsp. paratuberculosis, thus can be used fordetection of any of these three species. Further, probes to three TBgenes (Rv1980c.1, Rv1398c.1, and Rv2031c.1) can be used todifferentially identify M. tuberculosis, i.e., they will not detect M.avium or M. avium subsp. paratuberculosis. Probes to MAP 2121c.1, MAV3252.1, MAV 3239.1, and MAV 1600.1 can be used to detect M. avium or M.avium subsp. paratuberculosis, but will not detect M. tuberculosis.Thus, maximum sensitivity is achieved with the Rv1980c and Rv3853probes, while the probes to Rv1980c.1, Rv1398c.1, and Rv2031c.1, andMAP_(—)2121c.1, MAV_(—)3252.1, MAV_(—)3239.1, and MAV_(—)1600.1, enablethe distinction between M. tuberculosis infection and M. avium or M.avium subsp. paratuberculosis infection.

Probes were designed to genes both conserved throughout themycobacterium genus and specific only to Mycobacterium tuberculosis. Thepan-mycobacterial probes recognized multiple species, while the Mtuberculosis probes were highly specific (FIG. 22C).

Staphylococcus aureus and Stenotrophomonas maltophilia

Using the organism identification probes described Table 2, three S.aureus isolates were correctly identified using probes designed to fiveS. aureus genes (ileS, ppnK, pyrB, rocD, and uvrC) (FIG. 30). Similarly,three Stenotrophomonas maltophilia isolates were correctly identifiedusing probes designed to six S. maltophilia genes (clpP, dnaK, purC,purF, sdhA, and secD) (Table 2; and FIG. 31).

Example 2 Sensitivity of the Methods

As shown in FIGS. 7-10, the present methods are specific for eachpathogen of interest and sensitive to detect less than 100 cells inclinical samples, e.g., blood and urine.

RNA isolated from each of the three pathogens (1 ng) was probed with a24 gene probe set (FIG. 7). E. coli genes, left; K. pneumoniae genes,middle; and P. aeruginosa genes, right. E. coli RNA, top. K. pneumoniae,middle; and P. aeruginosa, bottom. The y-axis shows number of counts foreach gene as detected by using digital gene expression technology. RNAfrom each of the organisms shows distinct expression signatures thatallow facile identification of each of the pathogens.

This 24 gene probe set was used to probe crude E. coli lysates from10,000 cells, 1000 cells, and 100 cells (FIG. 8). The distinct E. coliexpression signature could be distinguished for down to 100 cells.

Clinical samples were simulated in spiked urine and blood samples. Inthe spiked urine sample, a urine sample was spiked with 105 E. colibacteria/mL of urine. The sample was refrigerated overnight at 4° C. andthen the crude bacterial sample was lysed and probed with the 24-geneprobe set used for the Gram negative bacteria to identify E. coli (FIGS.9A, top panel, and 9B). Blood was spiked with 1000 cfu/ml and alsodetected with the 24-gene probe set (FIG. 9A, bottom panel).

Two clinical isolates of P. aeruginosa (obtained from Brigham andWomen's clinical microbiology lab) were probed with the 24-gene probeset used for the Gram negative bacteria to demonstrate that the gene setis able to identify clinical diverse strains of the same bacterial genus(FIG. 10).

Identification of Escherichia coli directly in urine samples. E. colistrain K12 was grown in LB media at 37° C. to late log phase culture.Bacteria were then added to urine specimens from healthy donors to afinal concentration of 100,000 cfu/ml (as estimated by OD₆₀₀). Urinesamples were then left at room temperature for 0 hours, 4 hours, 24hours, or 48 hours or placed at 4° C. for 24 hours. 1 ml of spiked urinewas centrifuged at 13,000×g for 1 minute. The supernatant was removed;pellets were resuspended in 100 microliters of LB media. Bacteria weretreated with Bacteria RNase Protect (Qiagen), and then lysed inguianidinium isothiocyanate lysis buffer (RLT buffer, Qiagen). Lysateswere used in the nCounter™ System assays per manufacturer's protocol.

Aliquots of patient urine specimens were directly assayed to detect E.coli transcripts in urinary tract infections (FIG. 22D). To condense thesignals from multiple transcripts into a single metric that assesses thepresence or absence of an organism, the raw counts from each probe werelog transformed and summed. When applied to a set of 17 clinical E. coliisolates, every isolate was easily differentiated from a set of 13non-E. coli samples (Z score>6.5 relative to non-E. coli controls, FIG.22E).

Example 3 Drug Sensitivity of a Pathogen

Identification of fluoroquinolone and aminoglycoside resistance inEscherichia coli. Using published expression array data for E. coli uponexposure to fluoroquinolones and aminoglycosides (Sangurdekar D P,Srienc F, Khodursky A B. A classification based framework forquantitative description of large-scale microarray data. Genome Biol2006;7(4):R32) sets of genes expected to be significantly down- orup-regulated upon exposure to fluoroquinolones and aminoglycosides werechosen. The pan-sensitive lab strain (K12), fluoroquinolone-resistantclinical isolates 1 and 2, and gentamicin-resistant clinical isolates(E2729181 and EB894940) were grown in LB media to log phase at 37° C. A2 ml aliquot of each culture was taken, and antibiotics were added tothose aliquots at a final concentration of 8 μg/ml ciprofloxacin or 128μg/ml gentamicin. Cultures were incubated at 37° C. for 10 minutes. Fivemicroliters of each culture was added to 100 microliters of guanidiniumisothiocyanate lysis buffer and vortexed for 5 seconds. Lysates wereused in the nCounter™ System assays per manufacturer's protocol. Countswere normalized to counts of proC; again proC appeared to be mostcomparable between experiments; fold induction for each gene wasdetermined by comparing counts in the presence and absence of antibioticexposure. There were clear signals from 9 probes (carA, deoC, flgF,htrL, recA, uvrA, ybhK, uup, and fabD) that show induction or repressionin the drug sensitive K12 strain that distinguishes it from the tworesistant clinical isolates (FIG. 11). A tenth probe, wbbK, was neitherinduced nor repressed, offering a useful comparison for genes withchanges expression. Similarly, probes to eight genes show that thesegenes are either repressed (flgF, cysD, glnA, opgG) induced (ftsQ,b1649, recA, dinD) in the drug sensitive K12 strain that distinguishesit from the two resistant clinical isolates (FIG. 12)

Identification of methicillin resistance in Staphylococcus. Six S.aureus clinical isolates were grown to log phase at 37° C. in LB media.A 2 ml aliquot of each culture was then taken; cloxacillin was added toa final concentration of 25 μg/ml. Cultures were incubated at 37° C. for30 minutes. Five microliters of each culture was added to 100microliters of guanidinium isothiocyanate lysis buffer and vortexed for5 seconds. Lysates were used in the nCounter™ System assays permanufacturer's protocol. Using two independent probes (Table 2),expression of mecA was identified in the four isolates known to bemethicillin-resistant. In contrast, there was no detectable mecAexpression in the two isolates known to be methicillin-sensitive andminimal mecA expression in the absence of cloxacillin (FIG. 13).

Identification of vancomycin resistance in Enterococcus. FourEnterococcus clinical isolates were grown in LB media to log phase at37° C. A 2 ml aliquots were taken; vancomycin was added to a finalconcentration of 128 μg/ml. Cultures were incubated at 37° C. for 30minutes. Five microliters of each culture was added to 100 microlitersof guanidinium isothiocyanate lysis buffer and vortexed for 5 seconds.Lysates were used in the nCounter™ System assays per manufacturer'sprotocol. Using two independent probes (Table 2), expression of vanA wasidentified in the two isolates known to be vancomycin resistant. Incontrast, there was no detectable vanA expression in the two isolatesknown to be vancomycin sensitive and minimal expression of vanA in theabsence of vancomycin (FIG. 14). There was no detectable vanB expressionin any of the four isolates.

Beyond the detection of transcripts for organism identification,detection of genes encoded on mobile genetic elements can providegreater genomic detail about a particular isolate. For example,bacterial isolates were probed for mecA mRNA, which confers resistanceto methicillin in Staphylococci, and vanA mRNA, which confers resistanceto vancomycin in Enterococci. In both cases, relevant transcripts weredetected that allowed for rapid identification of MRSA andvancomycin-resistant Enterococcus (VRE) (FIG. 22F). Thus, directdetection of RNA is able to detect known resistance elements. Inaddition, this approach is able to discriminate isolates by othergenetic factors, such as virulence factors acquired through horizontalgenetic exchange in food-borne pathogens, i.e., Shiga toxin inEnterohemorrhagic or Shigatoxigenic E. coli.

Identification of drug resistance in TB. A 24 gene probe set wasidentified from published gene expression data to identify an expressionsignature that would allow identification of expression changes of drugsensitive TB upon exposure to different antibiotics, including isoniaid,rifampin, streptomycin, and fluoroquinolones (FIGS. 15-18). Themagnitude of induction or repression after drug exposure is shown inTable 3.

Log phase M. tuberculosis cells at A₆₀₀ 0.3 were grown in inkwellbottles (10 ml volume, parallel cultures) in the presence of one of fourdifferent drugs (isoniazid, 0.4 μg/ml; streptomycin, 2 μg/ml; ofloxacin,5 μg/ml; rifampicin 0.5 μg/ml). At the indicated time after theinitiation of drug treatment (FIG. 15), cultures were harvested bycentrifugation (3000×g, 5 minutes), resuspended in 1 ml Trizol, and beadbeaten (100 nm glass beads, max speed, two one-minute pulses).Chloroform (0.2 ml) was added to the samples, and following a fiveminute centrifugation at 6000×g, the aqueous phase was collected foranalysis.

Samples were diluted 1:10 and analyzed using NanoString™ probesetdescribed in Table 2 per the manufacturer's protocol. The relativeabundance of each transcript is first calculated by normalizing to theaverage counts of three housekeeping genes (sigA, rpoB, and mpt64), andthen the data is plotted as a fold change relative to samples fromuntreated controls. The boxes indicate probes that were selected basedon previous evidence of drug-specific induction (Boshoff et al., J BiolChem. 2004, 279(38):40174-84.)

The drug resistant TB strain shows no expression signature inductionupon exposure to isoniazid, in contrast to a drug sensitive strain,which clearly shows induction of an expression signature upon isoniazidexposure (FIG. 16). Three scatter plots comparing isoniazid sensitiveand resistant TB strains are shown in FIG. 16, with each dotrepresenting one of the 24 gene probes. The axes report number oftranscripts as measured by digital gene expression technology(NanoString™). Left—Comparison of expression in isoniazid resistant andisoniazid sensitive strains in the absence of drug treatment.Middle—Comparison of expression in drug treated vs. drug untreatedisoniazid sensitive strain. Right—Comparison of expression in drugtreated vs. drug untreated isoniazid resistant strain.

Different sets of genes are induced in drug-sensitive M. tuberculosisdepending on the drug as seen in FIG. 17. The transcriptional responsesof drug-sensitive and drug-resistant M. tuberculosis (A) Strain A50(INH-R) treated with INH (0.4 μg/ml) as described herein. (B) The SM-Rclone S10 was treated with 2 μg/ml streptomycin. Differential geneinduction can be measured by digital gene expression of the TB 24 geneprobe set to reveal a clear signature and allow identification of drugsensitivity (FIG. 18).

Three housekeeping genes, mpt64, rpoB, and sigA, were used fornormalization. For each experimental sample, the raw counts for theexperimental genes were normalized to the average of the raw counts ofthese three housekeeping genes, providing a measure of the abundance ofthe test genes relative to the control genes. Induction or repression isdefined as a change in these normalized counts in drug-exposed samplesas compared to non-drug-exposed samples. Using this methodology, thefollowing genes were found to be induced or repressed in drug-sensitiveTB after exposure to isoniazid, rifampin, fluoroquinolones, andstreptomycin.

Isoniazid: For drug-dependent induction: kasA, fadD32, accD6, efpA, andRv3675.1.

Rifampin: For drug-dependent induction: bioD, hisl, era, and Rv2296.

Fluoroquinolones: For drug-dependent induction: rpsR, alkA, recA, ltpl,and lhr; for drug-dependent repression: kasA and accD6.

Streptomycin: For drug-dependent induction: CHP, bcpB, gcvB, and groEL.

Example 4 A Phenotypic Expression Signature-Based Test to Identify DrugSensitive And Resistant TB Using Digital Gene Expression With MolecularBarcodes

This example describes a phenotypic expression-signature-based test forthe diagnosis of TB in sputum and rapid determination of resistanceprofile. The method is based on detection of genes whose expressionprofiles will uniquely detect TB and distinguish drug resistant andsensitive strains, with the creation of a probe set of bar-coded, pairedmolecular probes. The choice of genes was determined throughbioinformatic analysis of expression profile data obtained usingmicroarrays under a variety of growth conditions, including TB in axenicculture (both replicating and non-replicating states), TB in cellcultured macrophages, and TB spiked in sputum.

A. Define Signature for Identification of TB

A set of molecular probes have been identified that will specificallyhybridize to mRNA from both replicating and non-replicating TB. Theprobes are specific for mRNA that is highly abundant under all growthconditions and is conserved across all TB strains. While unique DNAsequences have been previously defined to identify TB recognizing 16SrRNA (Amplicor, Roche) or the IS6110 region (Gen-probe), these definedregions do not have the optimal characteristics required for signaturesin digital gene expression. The 16S rRNA is not sufficiently divergentamong mycobacterial species that could distinguish between the differentspecies using 50-base oligomer gene probes, which can tolerate lowlevels of genetic variability due to their length. The IS6110 region ofthe genome is not expressed at high enough levels under all growthconditions that would allow it to be used it as a robust signal toidentify TB. Thus, an expression signature that will allowidentification of TB from other mycobacterial species is described.

i. Bioinformatic gene analysis for conserved TB genes. Unique expressionsignatures for the detection of TB over other mycobacteria species havebeen defined. In general, the optimal genes for inclusion in a signaturewill fulfill the criteria of 1. having high expression levels (high mRNAcopy number) to increase sensitivity, 2. being highly conserved acrossall TB strains as well as having highly conserved sequence, and 3. beinghighly specific for TB genome over all other mycobacteria species. Suchgenes were identified using a bioinformatic analysis of conserved genesin the available TB genomes that are not present in all other sequencedmycobacteria species (i.e., M. marinum, M. avium-intracellulaire, M.kansaii, M. fortuitum, M. abscessus). Over 40 TB genomes from clinicallyisolated strains that have been sequenced at the Broad Institute areavailable for analysis.

ii. Expression profile analysis of mRNA levels of candidate genes. Asecond criterion for selection of molecular probes for the detection ofTB bacilli in sputum is that they hybridize to highly abundant, stablemRNAs to allow maximum sensitivity. Such mRNAs are anticipated tocorrespond to essential housekeeping genes. Genes have been selectedusing a combination of bioinformatic analysis of existing, publiclyavailable expression data in a database created at the Broad Instituteand Stanford University (tbdb.org) and experimental expression profileson TB strain H37Rv using expression profiling to confirm a high level ofexpression of candidate genes under conditions permissive forreplication (logarithmic growth) and non-replication induced by carbonstarvation, stationary phase, and hypoxia. Expression profilingexperiments on H37Rv are performed using a carbon starvation model of TBthat has been established (starvation for 5 weeks in 7H9/tyloxapol),stationary phase growth, and the Wayne model for anaerobic growth(slowly agitated cultures in sealed tubes). Solexa/Illumina sequencingis used to determine expression profiles by converting mRNA to cDNA andusing sequencing to count cDNA molecules. This quantitative method foridentifying expression levels is more likely to reflect levels obtainedusing digital gene expression than microarray data and is a method thathas been established with the Broad Institute Sequencing Platform. It ispossible to multiplex 12 samples per sequencing lane given 75 bp readsand 10 million reads per lane.

iii. Probe selection of expression signature identifying TB. Because thedigital gene expression technology is based on the hybridization of two50 nucleotide probes to the mRNA of interest, two 50 base pair regionsin the genes are identified from (Ai) and (Aii) that are unique withinthe genome to minimize non-specific hybridization and that containminimal polymorphisms as evidenced from sequenced TB genomes. The probesare selected bioinformatically to fit within a 5 degree meltingtemperature window and with minimal mRNA secondary structure. The probesare tested against mRNA isolated from replicating and non-replicating TB(including multiple strains i.e., H37Rv, CDC1551, F11, Erdman), M.marinum, M. avium-intracellulaire, M. kansaii, and M. fortuitum toconfirm the specificity of the entire probe set using availabletechnology. Probes may be selected for these other mycobacterialspecies, which will allow for identification of these pathogens fromsputum as well. The ability to identify intracellular bacilli is testedin a macrophage model of infection, to demonstrate the ability to detectTB mRNA in the presence of host mRNA. Finally, the sensitivity of theassay was determined by titrating down the number of TB bacilli (andthus mRNA present in cell lysates) in the sample tested. All experimentsusing digital gene expression is confirmed using quantitative RT-PCRagainst the same gene set. Improvement and refinement of the set willoccur in an iterative manner.

B. Define Signature to Distinguish Sensitive and Resistant TB

A set of molecular probes that hybridizes to mRNAs that are specificallyinduced upon exposure to each individual TB drug has been identified,allowing a profile to be obtained that distinguishes drug sensitive andresistant strains. Signatures have been determined for exposure toisoniazid, rifampin, ethambutol, streptomycin, and moxifloxacin.

In addition to the above characteristics for ideal genes to be includedin the signature (i.e., conserved across TB strains, specific for TBgenome), several other characteristics are prioritized in gene selectionfor signatures of drug resistance. Because drug resistance will bedetermined by the difference between transcript induction in drugsensitive and drug resistant strains, ideal gene candidates will behighly induced in drug sensitive strains upon exposure to a given drug.Ideally, these genes are induced early and quickly, as this time periodwill determine to a large extent, the rapidity of the overall diagnostictest. Based on data using qRT-PCR, a transcriptional response to drugexposure is observed in as little as 1-2 hours (FIG. 19). Given thehalf-lives of mRNA molecules, exploiting gene induction rather than generepression provides a more rapid and detectable response.

For all the described experiments involving isoniazid and streptomycin,TB strain H37Rv was used in a BSL3 setting in which a set of singlyresistant strains has been generated to be used to compare to thewild-type, fully drug sensitive H37Rv. To ensure that rifampin remains atreatment option in the unlikely event of a laboratory-acquiredinfection, rifampin resistant mutants will be generated in anauxotrophic strain of TB (lysA, panC) that requires the addition oflysine and pantothenate for growth.

Finally, signatures that are unique to each antibiotic have beenidentified rather than a general stress response to any or allantibiotics. The rationale for this specificity is that in a clinicalsetting, many patients will have already been empirically treated withdifferent antibiotics and thus some general stress response may havealready been activated in the bacilli within a sputum sample. However,drug specific responses are preserved for testing and analysis.

i. Expression profiling in response to antibiotic exposure. Expressionprofiling to identify candidate genes that distinguish transcriptionalresponses in drug sensitive and resistant strains of H37Rv has beenperformed. Because the replication state or transcriptional activity ofthe bacilli in sputum is unknown, additional experiments are performedon non-replicating (induced through a 5 week carbon starvation model)bacilli. It will determined if the non-replicating bacilli require ashort period (t₁) of “growth stimulation” in rich media (7H9/OADC) inorder to stimulate some basal transcription that can then be responsiveto drug exposure (FIG. 20). The optimal period of time (t₂) that isrequired for drug exposure in order to obtain robust signature todistinguish drug sensitive and resistant strains and the optimal drugconcentration is also determined to obtain a robust, reproducibleresponse. These experiments will be performed for each of the individualantibiotics.

A completely non-replicating state is the “worst case scenario” (i.e.,the longest period that would be required) if bacilli in sputum is in anon-replicating, dormant state. In fact, based on published workexamining expression profiles from bacilli in patient sputum, thisperiod will be extremely short if necessary at all, given thatexpression profiles were obtained directly from sputum bacilli. (Ofnote, this published data will also be incorporated into the analysis toprovide initial insight into possible gene candidates in bacteria insputum.) A matrix of profiling experiments are performed, varying thetime of exposure to rich 7H9/OADC media (ti) from 0, 1, and 2 hours; foreach t₁, and the time of exposure to each antibiotic (t₂). For each setof t₁ and t₂, the antibiotic concentration is varied from lx, 3×, and 5×the minimum inhibitory concentration (MIC) for each antibiotic, for bothsensitive and resistant H37Rv strains to determine the optimalparameters. Expression profiling will be used to identify optimalconditions for producing robust, reproducible profiles.

Based on the optimized conditions (t₁ and t₂), expression profiling isperformed on drug sensitive and resistant H37Rv strains under theseconditions. Bioinformatic analysis is performed to identify genes foreach drug in which the level of induction is high in drug sensitivestrains relative to drug resistant strains (with the exception ofrifampin in which the level of repression is high in drug sensitivestrains relative to drug resistant strains). The levels of expressionwill be compared between drug sensitive and drug resistant strains andconfirmed by quantitative RT-PCR.

ii. Develop analysis algorithm to identify drug resistance. An optimalalgorithm is determined to analyze expression ratios for sets of genesthat distinguish sensitive and resistant strains (as defined by standardMIC measurements). One of the strengths of this method is that for themajority of cases (i.e., those cases which have not been pre-exposed toTB antibiotics), a comparison can be done between the gene expressionlevels of the same strain not exposed and exposed to a given antibiotic.Quantitative RT-PCR is used to measure mRNA levels from H37Rv underconditions that include 1. exposure to no antibiotic, 2. exposure toisoniazid, 3. exposure to rifampin, 4. exposure to ethambutol, 5.exposure to streptomycin, and 6. exposure to moxifloxacin. The level ofexpression from a given gene after exposure to antibiotic will benormalized to the level of expression from a set of steady-state,housekeeping genes (i.e., sigA, which encodes the principal sigma factorthat stimulates the transcription of housekeeping genes, and rpoB, whichencodes a synthetic subunit of RNA polymerase) and compared to the samenormalized level of expression of the same gene in the absence ofantibiotic exposure. Comparisons will also be made to standard sensitiveand resistant control strains (FIG. 21). Ideally, exposure to aparticular drug will induce gene expression in drug sensitive strains tohigh levels, A>>B while for drug resistant strains, which areinsensitive to the drug exposure, A=B. (The exception will be forrifampin, in which gene repression of the mRNAs with shortest half-lifeis detected, given the mechanism of rifampin, i.e., A=C<<B=D.) Becauseof the large dynamic range of transcription levels, genes are selectedfor which the ratio of C/D is maximal, thus allowing for clear robustdifferentiation of sensitive and resistant strains. In addition,optimal, unique set of genes have been selected for each individualantibiotic so that there is no overlap in induced responses with otherantibiotics.

iii. Analysis of impact of pre-antibiotic exposure on TB bacillisignatures. To determine the efficacy of these signatures to identifyresistance patterns even in the event that a patient has beenpre-treated with antibiotics, drug sensitive and resistant TB bacilli(replicating, non-replicating, within macrophages) are pre-expose toamoxicillin, cephalosporins, trimethoprim-sulfamethoxazole, anderythromycin which are common antibiotics to which a patient may beexposed in TB endemic settings, prior to application of this test.Pre-exposure of TB bacilli to different combinations of current TB drugsis also performed to determine if such pre-exposure also interferes withthe transcriptional response our ability to detect such a response.Unique signatures should be preserved, thus not impairing our ability todetermine resistance. Gene expression levels of a set of genes ofinterest will be determined using quantitative RT-PCR.

iv. Probe selection of expression signature to identify resistanceprofile. Based on the data obtained in Sub-aim Bi, a set of candidategenes have been selected that will create a signature fortranscriptional response to antibiotic exposure. Two 50 base-pairregions for each gene are selected within regions that are highlyconserved across TB genomes. The probes are selected bioinformaticallyto fit within a 5 degree melting temperature window and with minimalmRNA secondary structure. These probes will be used to compare drugsensitive and resistant strains using available technology underconditions described above, including bacilli in axenic culture that areinitially replicating or nonreplicating, intracellular bacilli in a cellculture macrophage infection model that we have currently in ourlaboratory, and bacteria pre-exposed to different antibioticcombinations. All results will be compared to data obtained byquantitative RT-PCR. Improvement and refinement of the set will occur inan iterative manner.

C. Optimization of Sample Processing for Digital Gene Expression withMolecular Bar Codes.

In addition to defining probe sets for identification of expressionsignatures, the second major challenge is to optimize processing ofsamples in order to measure digital gene expression from bacilli presentwithin the sample. Because the majority of TB cases is pulmonary inorigin and the majority of samples to be processed is patient sputum,processing of sputum samples to obtain mRNA measurements from infectingTB bacilli is optimized. A spiked sputum model is used in which sputumcollected from healthy, uninfected patients (who have not been treatedwith antibiotics) was spiked with TB bacilli that are either in areplicating or non-replicating (carbon starved) state. Issues that willbe addressed include dealing with the variable viscosity of sputum andefficiently lysing the TB bacilli within a sputum sample. One of themajor advantages of digital gene expression is the ability to hybridizethe mRNAs to their respective probes in extremely crude samples,including crude cell lysates, fixed tissue samples, cells in whole bloodand urine, cells from crude lysates of ticks, and samples containing 400mM guanidinium isothiocyanate (GITC), polyacrylamide, and trizol. Thus,initial indications suggest that no purification step will be requiredafter lysing the bacteria within the sputum, as no purification has beenrequired from whole blood, urine, or fixed tissue samples. The onlyrequirement is sufficient mixing to allow contact between the probes andthe mRNAs.

For these experiments, uninfected sputum is obtained from the Brighamand Women's Hospital (BWH) specimen bank. The specimen bank is an IRBregulated unit directed by Lyn Bry, MD, PhD of the BWH pathologydepartment. Discarded sputum will be obtained after all processing iscompleted in the laboratory (generally within 12-24 hours ofcollection). Sputum is only collected from subjects who have notreceived any antibiotics in the previous 48 hours. All samples will bede-identified and no protected health information is collected. Based onthe current load of the specimen lab, the necessary amount of sputum(25-50 mL) is obtained within a matter of weeks.

i. Sputum processing. Sputum samples vary in bacterial load,consistency, and viscosity. Several approaches are tested to maximizethe rapidity with which the bacteria come into contact with bactericidallevels of antibiotic in media conditions and exposure to oligomer probesfor hybridization. Several methods of processing sputum, including noprocessing, passage of sputum through a syringe needle, treatment withlysozyme and/or DNase, Sputalysin (Calbiochem; 0.1% DTT) which isstandardly used to treat sputum from cystic fibrosis patients, or simpledilution of the sample into some minimal denaturant (i.e., GITC) areused. Sputum spiked with H37Rv and processed by a variety of methods toalter its viscosity are performed to determine if any of these methodsinterferes with the technology.

ii. Bacterial lysis in sputum spiked with TB bacilli. Several approachesto efficiently lyse bacterial cells, arrest transcription andenzyme-based mRNA degradation, and make mRNA accessible to the probesare used in the assay. Previous studies examining the transcriptionalresponses of bacteria in sputum have first added GTC or similar reagentsto the samples to arrest the transcriptional response. Centrifugationcan then be used to concentrate bacteria from sputum samples after GTCtreatment. Lysis of mycobacteria is generally accomplished throughphysical means, i.e. homogenization with 0.1 ml glass or zirconiumbeads. Such physical means are explored to disrupt the bacteria withinprocessed sputum to analyze bacilli that has been spiked into uninfectedhuman sputum using the designed probe set from 1A to detect TB bacilli.

Alternative methods are used for lysis that may be more amenable tofield-based considerations, including phage lysis. Addition of phage, ormore optimally, purified phage lysin(s), may provide a low-cost, simple,and non-electrical option for bacterial lysis. The Fischetti lab(Rockefeller University) has recently demonstrated the rapid andthorough lysis of several Gram-positive species using purifiedbacteriophage lysins, which enzymatically hydrolyse peptidoglycan,leading to osmotic lysis. The Hatfull lab (University of Pittsburgh) iscurrently working to characterize the activity and optimize theperformance of LysA enzymes from several lytic mycobacteriophages. Inthe absence of purified lysins, investigations are performed todetermine whether high MOI-infection of TB with a lytic bacteriophagesuch as D29 can efficiently lyse TB in sputum. It is currently unclearhow this approach will affect the transcriptional profile of thebacteria, since it will likely need to occur in the absence ofdenaturants that would impair the binding, entry, and subsequent lyticproperties of the phage. The mycobacteriophage TM4 also expresses astructural protein, Tmp, with peptidoglycan hydrolase activity, whichmay allow it to be used as a rapid means of cell lysis at high MOI. Oncelysed, the mRNA is stabilized with GITC, RNAlater, or other reagentsthat will inactivate endogenous RNAse activity.

Example 5

Bacterial and fungal culture: E. coli, K. pneumoniae, P. aeruginosa,Providencia stuartii, P. mirabilis, S. marcescens, E. aerogenes, E.cloacae, M morganii, K. oxytoca, C. freundii, or C. albicans were grownto an OD₆₀₀ of ˜1 in Luria-Bertani medium (LB). For mixing experiments,equal numbers of bacteria as determined by OD₆₀₀ were combined prior tolysis for NanoString™ analysis. Mycobacterium isolates were grown inMiddlebrook 7H9 medium to mid-log phase prior to harvest or antibioticexposure as described below.

Derivation of resistant laboratory bacterial strains: E. coli laboratorystrain J53 with defined fluoroquinolone-resistant chromosomal mutationsin gyrA (gyrA1-G81D; gyrA2 - S83L) were obtained from the Hooper lab,Massachusetts General Hospital, Boston, Mass. Plasma-mediated quinoloneresistance determinants (oqxAB, qnrB, aac6-Ib) were purified fromclinical isolates previously determined to contain these plasmids. E.coli parent strain J53 was transformed with these plasmids, and theirpresence was confirmed with PCR.

Viral and plasmodium infections: HeLa cells (1×10⁶), 293T cells (2×10⁵),and human peripheral blood monocytes (5×10⁵), were infected with HSV-1strain KOS and HSV-1 strain 186 Syn+, influenza A PR8, or HIV-1 NL-ADA,respectively, at the noted MOIs. Primary red blood cells (5×10⁹) wereinfected with P. falciparum strain 3D7 until they reached the notedlevels of parasitemia. At the indicated times, the cells were washedonce with PBS and harvested.

Antibiotic exposure: Cultures of E. coli or P. aeruginosa were grown toan OD₆₀₀ of ˜1 in LB. Cultures were then divided into two samples, oneof which was treated with antibiotic (E. coli for 10 minutes:ciprofloxacin 4-8 μg/ml or 300 ng/ml, gentamicin 64 or 128 μg/ml, orampicillin 500 μg/ml; P. aeruginosa for 30 minutes: ciprofloxacin 16μg/ml). Both treated and untreated portions were maintained at 37° C.with shaking at 200 rpm. Cultures of S. aureus or E. faecium were grownto an OD₆₀₀ of ˜1 in LB. Cultures were then exposed to cloxacillin (25μg/mL) or vancomycin (128 μg/mL), respectively, for 30 minutes.

Cultures of M. tuberculosis were grown to mid-log phase then normalizedto OD₆₀₀ of 0.2. 2 ml of each culture were treated with either noantibiotic or one of the following (final concentration): isoniazid0.2 - 1.0 μg/ml; streptomycin 5 μg/ml, rifampicin 0.5 μg/ml, orciprofloxacin 5 μg/ml. The plates were sealed and incubated withoutshaking for 3 or 6 hours. Lysates were then made and analyzed asdescribed above, using probes listed in Table 6.

Sample processing: For Gram negative isolates, 5-10 μl of culture wasadded directly to 100 μl RLT buffer and vortexed. For clinicalspecimens, 20 μl of urine from patients determined by a clinicallaboratory to have E. coli urinary tract infection was added directly to100 μl of RLT buffer. For mycobacteria, 1.5 ml of culture wascentrifuged, then resuspended in Trizol (Gibco) with or withoutmechanical disruption by bead beating, and the initial aqueous phase wascollected for analysis. Viral and parasite RNA were similarly preparedusing Trizol and chloroform. For all lysates, 3-5 μl were used directlyin hybridizations according to standard NanoString™ protocols. Rawcounts were normalized to the mean of all probes for a sample, and foldinduction for each gene was determined by comparing antibiotic-treatedto untreated samples.

Selection of organism identification probes: To select NanoString™probes for differential detection of organisms, all publically availablesequenced genomes for relevant organisms were compared. Genes conservedwithin each species were identified by selecting coding sequences (CDS)having at least 50% identity over at least 70% of the CDS length for allsequenced genomes for that species. The CDS was broken into overlapping50-mers and retained only those 50-mers perfectly conserved within aspecies and having no greater than 50% identity to a CDS in any otherspecies in the study. Available published expression data in GeneExpression Omnibus was reviewed, and genes with good expression undermost conditions were selected. To identify unique M. tuberculosisprobes, published microarray data was used to identify highly expressedgenes falling into one of two classes: those unique to the M.tuberculosis complex (>70% identity to any other gene in thenon-redundant database using BLASTN and conserved across all availableM. tuberculosis and M. bovis genomes), as well as those with >85%identity across a set of clinically relevant mycobacteria including M.tuberculosis, M. avium, and M. paratuberculosis. C. albicans probes weredesigned against 50-mer segments of C. albicans genome unique incomparison with the complete genomes of ten additional pathogenicorganisms that were included in its probe set. Viral probes weredesigned against highly conserved genes within a virus (i.e. all HSV-2or HIV-1 isolates) that were less conserved among viruses within thesame family, (i.e between HSV-1 and HSV-2). Plasmodium falciparum probeswere designed against genes expressed abundantly in each of the bloodstages of the parasite life cycle. All probes were screened to avoidcross hybridization with human RNA.

Probe Sets: For Gram-negative organism identification, a pooledprobe-set containing probes for E. coli, K. pneumoniae, and P.aeruginosa were used. For mycobacterial organism identification,species-specific probes for M. tuberculosis and broader mycobacterialgenus probes were among a larger set of probes against microbialpathogens.

Probes were designed for genes that are differentially regulated uponexposure to various antimicrobial agents to measure the presence orabsence of a response (Sangurdekar et al., Genome Biology 7, R32 (2006);Anderson et al., Infect. Immun. 76, 1423-1433, (2008); Brazas andHancock, Antimicrob. Agents Chemother. 49, 3222-3227, (2005)). Following10-30 minute exposures of wild-type E. coli K-12 to ciprofloxacin,gentamicin, or ampicillin, the expected changes in transcript levelsthat together define the drug-susceptible expression signature for eachantibiotic were observed (FIGS. 23A and 23B, Table 7). These signatureswere not elicited in the corresponding resistant strains (FIGS. 23A and23B).

Rapid phenotypic drug-susceptibility testing would make a particularlyprofound impact in tuberculosis, as established methods for phenotypictesting take weeks to months (Minion et al., Lancet Infect Dis 10,688-698, (2010)). Expression signatures in response to anti-tubercularagents isoniazid, ciprofloxacin, and streptomycin were able todistinguish susceptible and resistant isolates after a 3 to 6 hourantibiotic exposure (FIG. 23C). Some genes in the transcriptionalprofiles are mechanism-specific (i.e., recA, alkA, and lhr forciprofloxacin; groEL for streptomycin; and kasA and accD6 forisoniazid). Other genes, particularly those involved in mycolic acidsynthesis or intermediary metabolism, are down-regulated in response tomultiple antibiotics, indicating a shift away from growth towards damagecontrol.

To condense these complex responses into a single, quantitative metricto distinguish susceptible and resistant strains, the metric of themean-squared distance (MSD) of the expression response was utilized fromeach experimental sample from the centroid of control,antibiotic-susceptible samples. Antibiotic-susceptible strains clusterclosely, thus possessing small MSDs. Conversely, antibiotic-resistantstrains have larger values, the result of numerous genes failing torespond to antibiotic in a manner similar to the average susceptiblestrain. MSD is reported as dimensionless Z-scores, signifying the numberof standard deviations a sample lies from the average of sensitiveisolates of E. coli (FIGS. 24A, 24B, 27, and 28) or M. tuberculosis(FIGS. 24C and 29).

Because expression profiles reflect phenotype rather than genotype,resistance mediated by a variety of mechanisms can be measured using asingle, integrated expression signature. The transcriptional responsesof ciprofloxacin-susceptible E. coli strain J53 were compared with aseries of isogenic mutants with different mechanisms of resistance: twowith single mutations in the fluoroquinolone-target gene topoisomerasegyrA (G81D or S83L) and three carrying episomal quinolone resistancegenes including aac(6′)-Ib (an acetylating, inactivating enzyme), qnrB(which blocks the active site of gyrA), and oqxAB (an efflux pump). Incomparison with the parent strain, all J53 derivatives had largeZ-scores, reflective of resistance (FIG. 24B).

Response to isoniazid was compared in a series of sensitive clinical andlaboratory isolates and two isoniazid resistant strains, including anH37Rv-derived laboratory strain carrying a mutation in katG (S315T), acatalase necessary for pro-drug activation, and a clinical isolate witha mutation in the promoter of inhA (C-15T), the target of isoniazid. Dueto their disparate resistance mechanisms, these two strains havediffering levels of resistance to isoniazid, with the katG mutantpossessing high level resistance (>100-fold increase in minimalinhibitory concentration (MIC) to >6.4 μg/mL), while the inhA promotermutation confers only an 8-fold increase in the MIC to 0.4 μg/mL.Exposure to low isoniazid concentrations (0.2 μg/mL) failed to elicit atranscriptional response in either resistant strain, but at higherisoniazid concentrations (1 μg/mL), the inhA mutant responds in asusceptible manner in contrast to the katG mutant (FIG. 24C). Thus, thismethod is not only mechanism-independent, but can also provide arelative measure to distinguish high and low-level resistance.

Finally, because RNA is almost universal in pathogens ranging frombacteria, viruses, fungi, to parasites, RNA detection can be integratedinto a single diagnostic platform applicable across a broad range ofinfectious agents. Using a large pool of mixed pathogen probes, we wereable to directly and specifically detect signals to identify the fungalpathogen Candida albicans (FIG. 25A); human immunodeficiency virus(HIV), influenza virus, and herpes simplex virus-2 (HSV-2) in cellculture in a dose dependent manner (FIGS. 25B-D); and the differentstages of the Plasmodium falciparum life cycle in infected erythrocytes(FIG. 25E).

NanoString™ data analysis and calculation of distance metric meansquared distance for drug-sensitivity: For all drug-treated samples, rawNanoString™ counts for each probe were first normalized to the mean ofall relevant (i.e., species-appropriate) probes for each sample.Fold-change in transcript levels was determined by comparing thenormalized counts for each probe in the antibiotic-treated samples withthe corresponding counts in the untreated baseline sample for each testcondition.

To transform qualitative expression signatures into a binary outcome ofsensitive or resistant, an algorithm was developed to calculate meansquared distance (MSD) of a sample's transcriptional profile from thatof sensitive strains exposed to the same drug. The MSD metric indrug-sensitivity experiments was calculated as follows:

1. Variation in sample amount is corrected for by normalizing raw valuesto the average number of counts for all relevant probes in a sample.

2. A panel of NanoString™ probes, which we denote is selected. Thesubscript j runs from 1 to N_(probes), the total number of selectedprobes. The analysis is restricted to probes that changed differentiallybetween drug-sensitive and drug-resistant isolates.

3. Replicates of the drug-sensitive strain are defined as N_(samp). Foreach replicate, normalized counts for each probe P_(j) before or afterdrug treatment were denoted C_(i,p,) _(j) ^(before) or C_(i,p,) _(j)^(after) , with i signifying the sample index.

4. “Log induction ratio” is next computed:

S_(i,P) _(j) ≡In[C_(l,P) _(j) ^(before)/C_(l,P) _(j) ^(after)]

Log transforming the ratio in this way prevents any single probe fromdominating the calculated MSD.

5. The average induction ratio of the drug sensitive samples, S

, is calculated by summing over the different biological replicates andnormalizing by the number of samples:

${S\text{?}} = \frac{\sum\limits_{\text{?} = \text{?}}^{\text{?}}\; {S\text{?}}}{N\text{?}}$?indicates text missing or illegible when filed

6. MSD is next calculated for the each of the replicates of the drugsensitive strain (of index i), a number that reflects how different asample is from the average behavior of all drug sensitive samples:

${{MSD}\text{?}} = \frac{\sum\limits_{\text{?} = \text{?}}^{\text{?}}\; {\left( {S\text{?}\mspace{14mu} \overset{\_}{S}\text{?}} \right)\text{?}}}{N\text{?}}$?indicates text missing or illegible when filed

7. Induction ratios for resistant strains, R_(i,P) _(j) , are calculatedsimilarly to those of sensitive strains:

R_(i,P) _(j) ≡IN[C_(i,P) _(j) ^(before)/C_(i,P) _(j) ^(after)]

8. The MSD for the drug resistant strains is calculated relative to thecentroid of the drug-sensitive population:

${{MSD}\text{?}} = \frac{\sum\limits_{\text{?} = \text{?}}^{\text{?}}\; {\left( {R\text{?}\mspace{14mu} \overset{\_}{R}\text{?}} \right)\text{?}}}{N\text{?}}$?indicates text missing or illegible when filed

Because most sensitive strains behave similarly to the average sensitivestrain the typical value for MSD

is small compared to the typical value for a resistant strain, MSD

.

Finally, statistical significance of the measured MSD values wereassigned. Because the MSD

values are the sum of a number of random deviations from a mean, theyclosely resemble a normal distribution, a consequence of the CentralLimit Theorem.

Therefore, z-scores, which reflect the number of standard deviationsaway a given sample is relative to the drug sensitive population, werecomputed for each sample:

${Z\text{?}} = \frac{{MSD}\text{?}\mspace{14mu} \overset{\_}{{MSD}\text{?}}}{\sigma \text{?}}$?indicates text missing or illegible when filed

where the standard deviations and means are defined as:

${\sigma \text{?}} = \sqrt{\frac{1}{N\text{?}}{\sum\limits_{\text{?} = \text{?}}^{\text{?}}\; {\left( {{MSD}\text{?}\mspace{14mu} \overset{\_}{{MSD}\text{?}}} \right)\text{?}}}}$?indicates text missing or illegible when filed

and:

$\overset{\_}{{MSD}\text{?}} = {\sum\limits_{\text{?} = \text{?}}^{\text{?}}\; {{MSD}\text{?}}}$?indicates text missing or illegible when filed

This metric was applied to the analysis of numerous laboratory andclinical isolates that were tested against different antibiotics and thedata are shown in FIGS. 24, 27, 28, and 29.

Calculation of distance metric for organism identification: To transformthe information from multiple probes into a binary outcome, raw countsfor each probe were log-transformed. Log transforming the ratio in thisway prevents any single probe from dominating the analysis. Theselog-transformed counts were then averaged between technical replicates.

A panel of NanoString™ probes, which are denoted P_(j), is selected asdescribed. The subscript j runs from 1 to N_(probes), the total numberof selected probes.

S_(i,P) _(J) ≡In[C_(i,P) _(j) ]

Because organism identification depends on an ability to detecttranscripts relative to mocks or different organisms, background levelof NanoString™ counts in samples prepared without the organism ofinterest was thus used to define a control centroid. The centroid ofthese control samples, S, is calculated by summing over the differentbiological replicates and normalizing by the number of samples:

${S\text{?}} = \frac{\sum\limits_{\text{?} = \text{?}}^{\text{?}}\; {S\text{?}}}{N\text{?}}$?indicates text missing or illegible when filed

MSD is next calculated for the averaged technical replicates of theexperimental samples (of index i), a number that reflects how differenta sample is from the average behavior of all control samples:

${{MSD}\text{?}} = \frac{\sum\limits_{\text{?} = \text{?}}^{\text{?}}\; {\left( {S\text{?}\mspace{14mu} \overset{\_}{S}\text{?}} \right)\text{?}}}{N\text{?}}$?indicates text missing or illegible when filed

Finally, statistical significance was assigned to the measured MSDvalues.

Because the MSD

values are the sum of a number of random deviations from a mean, theyclosely resemble a normal distribution, a consequence of the CentralLimit Theorem. We therefore computed z-scores for each sample, whichreflect the number of standard deviations away a given sample isrelative to the control population:

${Z\text{?}} = \frac{{MSD}\text{?}\mspace{14mu} \overset{\_}{{MSD}\text{?}}}{\sigma \text{?}}$?indicates text missing or illegible when filed

where the standard deviations and means are defined as:

${\sigma \text{?}} = \sqrt{\frac{1}{N\text{?}}{\sum\limits_{\text{?} = \text{?}}^{\text{?}}\; {\left( {{MSD}\text{?}\mspace{14mu} \overset{\_}{{MSD}\text{?}}} \right)\text{?}}}}$?indicates text missing or illegible when filed

and:

$\overset{\_}{{MSD}\text{?}} = {\sum\limits_{\text{?} = \text{?}}^{\text{?}}\; {{MSD}\text{?}}}$?indicates text missing or illegible when filed

This metric was applied to the analysis of numerous laboratory strainsand clinical isolates that were tested for the relevant bacterialspecies as shown in FIG. 26 and Table 4. A strain was identified as aparticular organism if the MSD>2 for that organism.

TABLE 4 Numbers of laboratory and clinical isolates tested with organismidentification probes. Organism Laboratory strains tested Clinicalisolates tested E. coli 2 17 K. pneumoniae 0 4 P. aeruginosa 1 9 M.tuberculosis 1 10

TABLE 5 Genes used for bacterial organism identification. Organism GeneAnnotated function E. coli ftsQ Divisome assembly murC Peptidoglycansynthesis putP Sodium solute symporter uup Subunit of ABC transporteropgG Glucan biosynthesis K. pneumoniae mraW S-adenosyl-methyltransferaseihfB DNA-binding protein clpS Protease adaptor protein lrpTranscriptional regulator P. aeruginosa mpl Ligase, cell wall synthesisproA Gamma-glutamyl phosphate reductase dacC Carboxypeptidase, cell wallsynthesis lipB Lipoate protein ligase sltB1 Transglycosylase ConservedcarD Transcription factor Mycobacterium infC Translation initiationfactor M. tuberculosis Rv1398c Hypothetical protein mptA Immunogenicprotein 64 hspX Heat shock protein

TABLE 6 Laboratory and clinical isolates tested for susceptibilityprofiling. Clinical isolates are designated CI. Sensitive (S) OrganismAntibiotic or Resistant (R) Strain MIC* E. coli Cipro- S K12 30 ng/mlfloxacin S J53 30 ng/ml S CIEC9955 <0.1 μg/ml S CICr08 <.1 μg/ml RCIEC1686 50 μg/ml R CIEC9779 50 μg/ml R CIEC0838 50 μg/ml R CIqnrS 6.25μg/ml R CIaac6-Ib >100 μg/ml R CIqnrA 12.5 μg/ml R CIqnrB 6.25 μg/ml E.coli Gentamicin S K12 8 μg/ml S CIEC1676 8 μg/ml S CIEC9955 16 μg/ml SCIEC1801 8 μg/ml R CIEC4940 >250 μg/ml R CIEC9181 >250 μg/ml R CIEC2219125 μg/ml E. coli Ampicillin S K12 4 μg/ml J53 4 μg/ml DH5α 8 μg/ml RCIEC9955 >250 μg/ml CIEC2219 >250 μg/ml CIEC0838 >250 μg/mlCIEC9181 >250 μg/ml P. aeruginosa Cipro- S PAO-1 1 μg/ml floxacin SCIPA2085 0.4 μg/ml S CIPA1189 0.4 μg/ml S CIPA9879 0.4 μg/ml R CIPA223350 μg/ml R CIPA1839 25 μg/ml R CIPA1489 25 μg/ml M. tuberculosisIsoniazid S H37Rv 0.05 μg/ml S AS1 (CI) <0.2 μg/ml S AS2 (CI) <0.2 μg/mlS AS3 (CI) <0.2 μg/ml S AS4 (CI) <0.2 μg/ml S AS5 (CI) <0.2 μg/ml S AS10(CI) <0.2 μg/ml R A50 >6.25 μg/ml R BAA-812 0.4 μg/ml M. tuberculosisCipro- S mc²6020 0.5 μg/ml floxacin S AS1 (CI) <1 μg/ml S AS2 (CI) <1μg/ml S AS3 (CI) <1 μg/ml S AS4 (CI) <1 μg/ml S AS5 (CI) <1 μg/ml S AS10(CI) <1 μg/ml R C5A15 16 μg/ml M. tuberculosis Streptomycin S H37Rv 1μg/ml S AS1 (CI) <2 μg/ml S AS2 (CI) <2 μg/ml S AS3 (CI) <2 μg/ml S AS4(CI) <2 μg/ml S AS5 (CI) <2 μg/ml R CSA1 >32 μg/ml

TABLE 7 Genes associated with antibiotic sensitivity signatures in E.coli, P. aeruginosa, and M. tuberculosis. Organism Antibiotic GeneAnnotated function E. coli Ciprofloxacin dinD DNA-damage inducibleprotein recA DNA repair, SOS response uvrA ATPase and DNA damagerecognition protein uup predicted subunit of ABC transporter GentamicinpyrB aspartate carbamoyltransferase recA DNA repair, SOS response wbbKlipopolysaccharide biosynthesis Ampicillin hdeA stress response proCpyrroline reductase opgG glucan biosynthesis P. aeruginosa CiprofloxacinPA_4175 probable endoprotease mpl peptidoglycan biosynthesis proAGlutamate-semialdehyde dehydrogenase M. tuberculosis Ciprofloxacin lhrhelicase rpsR ribosomal protein S18-1 ltp1 lipid transfer alkA baseexcision repair recA recombinase kasA mycolic acid synthesis accD6mycolic acid synthesis Isoniazid efpA efflux pump kasA mycolic acidsynthesis accD6 mycolic acid synthesis Rv3675 Possible membrane proteinfadD32 mycolic acid synthesis Streptomycin Rv0813 conserved hypotheticalprotein groEL Heat shock protein bcpB peroxide detoxification gcvBglycine dehydrogenase accD6 mycolic acid synthesis kasA mycolic acidsynthesis

The direct measurement of RNA expression signatures described herein canprovide rapid identification of a range of pathogens in culture anddirectly from patient specimens. Significantly, phenotypic responses toantibiotic exposure can distinguish susceptible and resistant strains,thus providing an extremely early and rapid determination ofsusceptibility that integrates varying resistance mechanisms into acommon response. This principle represents a paradigm shift in whichpathogen RNA forms the basis for a single diagnostic platform that couldbe applicable in a spectrum of clinical settings and infectiousdiseases, simultaneously providing pathogen identification and rapidphenotypic antimicrobial susceptibility testing.

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Other Embodiments

It is to be understood that while the invention has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention, which is defined by the scope of the appended claims. Otheraspects, advantages, and modifications are within the scope of thefollowing claims.

1-4. (canceled)
 5. A method of identifying an infectious diseasepathogen, the method comprising: providing a test sample from a subjectsuspected of being infected with a pathogen; treating the test sampleunder conditions that release messenger ribonucleic acid (mRNA);exposing the test sample to a plurality of nucleic acid probes designedto identify a plurality of pathogens, comprising a plurality of subsetsof probes, wherein each subset comprises one or more probes that bindspecifically to a target mRNA that uniquely identifies a singlepathogen, wherein the exposure occurs for a time and under conditions inwhich binding between the probe and the target mRNA can occur; anddetermining a level of binding between the probe and target mRNA,thereby determining a level of target mRNA; wherein an increase in thetarget mRNA of the test sample, relative to a reference sample,indicates the identity of the pathogen in the test sample.
 6. The methodof claim 1, wherein the test sample is selected from the groupconsisting of sputum, blood, urine, stool, joint fluid, cerebrospinalfluid, and cervical/vaginal swab.
 7. The method of claim 1, wherein thetest sample comprises a plurality of different infectious diseasepathogens or non-disease causing organisms.
 8. The method of claim 1,wherein the one or more nucleic acid probes are selected from Table 2.9. The method of claim 1, wherein the pathogen is a bacterium, fungus,virus, or parasite.
 10. The method of claim 1, wherein the pathogen isMycobacterium tuberculosis.
 11. The method of claim 1, wherein the mRNAis crude before contact with the probes.
 12. The method of claim 1,wherein the method does not include amplifying the mRNA.
 13. The methodof claim 1, wherein the method comprises lysing the cells enzymatically,chemically, or mechanically.
 14. The method of claim 1, wherein themethod comprises use of a microfluidic device.
 15. The method of claim1, wherein the method is used to monitor a pathogen infection. 16.(canceled)
 17. The method of claim 5, wherein the subject is a human.18. The method of claim 5, wherein the method further comprisesdetermining or selecting, a treatment for the subject, and optionallyadministering the treatment to the subject. 19-21. (canceled)
 22. Themethod of claim 18, wherein the method comprises selecting a treatmentto which the pathogen is sensitive and administering the selectedtreatment to the subject, and determining the drug sensitivity of thepathogen in the second sample to the selected treatment using the methodof claim 1, wherein a change in the drug sensitivity of the pathogenindicates whether the pathogen is or is becoming resistant to thetreatment. 23-24. (canceled)
 25. A plurality of polynucleotides bound toa solid support, wherein the plurality comprises at least onepolynucleotide, each polynucleotide selectively hybridizing to one ormore genes selected from Table
 2. 26. The plurality of polynucleotidesof claim 25, the plurality comprising SEQ ID NOs:1-227, or anycombination thereof.